feat(lidar): qualify Patchwork++ on GOOSE

apps/control-station/src/core/lidar/datasetGateway.ts

@@ -90,15 +90,7 @@ export interface DatasetNativeScanPreview {
   }>;
 }
 
-export interface DatasetGroundComparison {
+export interface DatasetGroundMetrics {
-  sourceId: "goose-3d/v2025-08-22";
-  frameId: string;
-  pointCount: number;
-  currentGround: number[];
-  groundTruthGround: number[];
-  evaluated: number[];
-  disagreement: number[];
-  metrics: {
   precision: number;
   recall: number;
   f1: number;
@@ -107,8 +99,34 @@ export interface DatasetGroundComparison {
   artificialGroundRecall: number;
   naturalGroundRecall: number;
   obstacleNonGroundRecall: number;
+}
+
+export interface DatasetGroundComparison {
+  sourceId: "goose-3d/v2025-08-22";
+  frameId: string;
+  pointCount: number;
+  currentGround: number[];
+  patchworkGround: number[];
+  patchworkAssigned: number[];
+  groundTruthGround: number[];
+  evaluated: number[];
+  currentDisagreement: number[];
+  patchworkDisagreement: number[];
+  metrics: {
+    current: DatasetGroundMetrics;
+    patchworkpp: DatasetGroundMetrics;
+  };
+  latencyMs: {
+    current: number;
+    patchworkpp: number;
+  };
+  patchworkAssignedFraction: number;
+  inputProfile: {
+    frameId: "sensor/lidar/vls128_roof";
+    sensorHeightM: number;
+    heightEvidence: "published-dimensioned-schematic";
+    normalizedScanProduced: false;
   };
-  latencyMs: number;
 }
 
 export class DatasetGatewayContractError extends Error {}
@@ -500,7 +518,7 @@ export function parseDatasetGroundComparison(
 ): DatasetGroundComparison {
   const source = record(value, "Ground comparison");
   if (
-    source.schema_version !== "missioncore.dataset-ground-comparison-preview/v1"
+    source.schema_version !== "missioncore.dataset-ground-comparison-preview/v2"
     || source.source_id !== "goose-3d/v2025-08-22"
     || source.sampling !== "deterministic-even-index"
   ) {
@@ -508,41 +526,135 @@ export function parseDatasetGroundComparison(
   }
   const pointCount = number(source.point_count, "point_count");
   const currentGround = integers(source.current_ground, "current_ground", 1);
+  const patchworkGround = integers(source.patchwork_ground, "patchwork_ground", 1);
+  const patchworkAssigned = integers(
+    source.patchwork_assigned,
+    "patchwork_assigned",
+    1,
+  );
   const groundTruthGround = integers(
     source.ground_truth_ground,
     "ground_truth_ground",
     1,
   );
   const evaluated = integers(source.evaluated, "evaluated", 1);
-  const disagreement = integers(source.disagreement, "disagreement", 1);
+  const currentDisagreement = integers(
+    source.current_disagreement,
+    "current_disagreement",
+    1,
+  );
+  const patchworkDisagreement = integers(
+    source.patchwork_disagreement,
+    "patchwork_disagreement",
+    1,
+  );
   if (
     pointCount < 1
     || pointCount > 50_000
     || currentGround.length !== pointCount
+    || patchworkGround.length !== pointCount
+    || patchworkAssigned.length !== pointCount
     || groundTruthGround.length !== pointCount
     || evaluated.length !== pointCount
-    || disagreement.length !== pointCount
+    || currentDisagreement.length !== pointCount
+    || patchworkDisagreement.length !== pointCount
   ) {
     throw new DatasetGatewayContractError("Ground comparison arrays не выровнены");
   }
   const metrics = record(source.metrics, "metrics");
+  const parseMetrics = (
+    value: unknown,
+    label: string,
+  ): DatasetGroundMetrics => {
+    const providerMetrics = record(value, label);
     const fraction = (key: string): number => {
-    const value = number(metrics[key], `metrics.${key}`);
+      const result = number(providerMetrics[key], `${label}.${key}`);
-    if (value > 1) {
+      if (result > 1) {
-      throw new DatasetGatewayContractError(`metrics.${key}: ожидалась доля`);
+        throw new DatasetGatewayContractError(`${label}.${key}: ожидалась доля`);
       }
-    return value;
+      return result;
     };
-  const provider = record(source.provider, "provider");
+    return {
+      precision: fraction("precision"),
+      recall: fraction("recall"),
+      f1: fraction("f1"),
+      groundIou: fraction("ground_iou"),
+      accuracy: fraction("accuracy"),
+      artificialGroundRecall: fraction("artificial_ground_recall"),
+      naturalGroundRecall: fraction("natural_ground_recall"),
+      obstacleNonGroundRecall: fraction("obstacle_non_ground_recall"),
+    };
+  };
+  const currentMetrics = parseMetrics(metrics.current, "metrics.current");
+  const patchworkMetrics = parseMetrics(
+    metrics.patchworkpp,
+    "metrics.patchworkpp",
+  );
+  const providers = record(source.providers, "providers");
+  const currentProvider = record(providers.current, "providers.current");
+  const patchworkProvider = record(providers.patchworkpp, "providers.patchworkpp");
   if (
-    provider.provider_id !== "missioncore-local-percentile-ground/v1"
+    currentProvider.provider_id !== "missioncore-local-percentile-ground/v1"
-    || provider.ground_truth !== false
+    || currentProvider.ground_truth !== false
     || !/^[a-f0-9]{64}$/.test(
-      string(provider.implementation_sha256, "provider.implementation_sha256"),
+      string(
+        currentProvider.implementation_sha256,
+        "providers.current.implementation_sha256",
+      ),
+    )
+    || patchworkProvider.provider_id !== "patchworkpp/v1.4.1"
+    || patchworkProvider.source_tag !== "v1.4.1"
+    || patchworkProvider.source_commit
+      !== "3e6903a1d5537a4cc2ace897b0bbb98a92d6014c"
+    || patchworkProvider.ground_truth !== false
+    || !/^[a-f0-9]{64}$/.test(
+      string(
+        patchworkProvider.binary_sha256,
+        "providers.patchworkpp.binary_sha256",
+      ),
     )
   ) {
     throw new DatasetGatewayContractError("Ground comparison provider несовместим");
   }
+  const latency = record(source.latency_ms, "latency_ms");
+  const inputProfile = record(source.input_profile, "input_profile");
+  const sensorFrame = record(inputProfile.sensor_frame, "input_profile.sensor_frame");
+  const height = record(inputProfile.height, "input_profile.height");
+  const scope = record(inputProfile.scope, "input_profile.scope");
+  const sensorHeightM = number(
+    height.sensor_above_ground_m,
+    "height.sensor_above_ground_m",
+  );
+  if (
+    inputProfile.schema_version !== "missioncore.goose-patchwork-profile/v1"
+    || inputProfile.source_id !== "goose-3d/v2025-08-22"
+    || inputProfile.representation !== "native-scan"
+    || sensorFrame.frame_id !== "sensor/lidar/vls128_roof"
+    || sensorFrame.handedness !== "right"
+    || sensorFrame.x !== "forward"
+    || sensorFrame.y !== "left"
+    || sensorFrame.z !== "up"
+    || sensorFrame.one_revolution !== true
+    || height.base_link_above_ground_m !== 0.64
+    || height.lidar_above_base_link_m !== 1.6
+    || sensorHeightM !== 2.24
+    || height.evidence !== "published-dimensioned-schematic"
+    || scope.patchworkpp_eligible !== true
+    || scope.normalized_scan_produced !== false
+    || scope.complete_vehicle_transform_known !== false
+    || scope.deskew_claimed !== false
+  ) {
+    throw new DatasetGatewayContractError("GOOSE Patchwork++ profile несовместим");
+  }
+  const patchworkAssignedFraction = number(
+    source.patchwork_assigned_fraction,
+    "patchwork_assigned_fraction",
+  );
+  if (patchworkAssignedFraction > 1) {
+    throw new DatasetGatewayContractError(
+      "patchwork_assigned_fraction: ожидалась доля",
+    );
+  }
   const safety = record(source.safety, "safety");
   if (
     safety.qualification_only !== true
@@ -555,20 +667,27 @@ export function parseDatasetGroundComparison(
     frameId: string(source.frame_id, "frame_id", true),
     pointCount,
     currentGround,
+    patchworkGround,
+    patchworkAssigned,
     groundTruthGround,
     evaluated,
-    disagreement,
+    currentDisagreement,
+    patchworkDisagreement,
     metrics: {
-      precision: fraction("precision"),
+      current: currentMetrics,
-      recall: fraction("recall"),
+      patchworkpp: patchworkMetrics,
-      f1: fraction("f1"),
+    },
-      groundIou: fraction("ground_iou"),
+    latencyMs: {
-      accuracy: fraction("accuracy"),
+      current: number(latency.current, "latency_ms.current"),
-      artificialGroundRecall: fraction("artificial_ground_recall"),
+      patchworkpp: number(latency.patchworkpp, "latency_ms.patchworkpp"),
-      naturalGroundRecall: fraction("natural_ground_recall"),
+    },
-      obstacleNonGroundRecall: fraction("obstacle_non_ground_recall"),
+    patchworkAssignedFraction,
+    inputProfile: {
+      frameId: "sensor/lidar/vls128_roof",
+      sensorHeightM,
+      heightEvidence: "published-dimensioned-schematic",
+      normalizedScanProduced: false,
     },
-    latencyMs: number(source.latency_ms, "latency_ms"),
   };
 }
 

apps/control-station/src/workspaces/DatasetGatewayWorkspace.tsx

@@ -138,9 +138,11 @@ export function DatasetGatewayWorkspace() {
       masks: {
         currentGround: comparisonAligned?.currentGround ?? emptyMask,
         currentAssigned: comparisonAligned?.evaluated ?? emptyMask,
-        candidateGround: comparisonAligned?.groundTruthGround ?? emptyMask,
+        candidateGround: comparisonAligned?.patchworkGround ?? emptyMask,
-        candidateAssigned: comparisonAligned?.evaluated ?? emptyMask,
+        candidateAssigned: comparisonAligned?.patchworkAssigned ?? emptyMask,
-        disagreement: comparisonAligned?.disagreement ?? emptyMask,
+        disagreement: comparisonAligned?.currentDisagreement ?? emptyMask,
+        candidateDisagreement:
+          comparisonAligned?.patchworkDisagreement ?? emptyMask,
         groundTruthGround: preview.groundTruthGround,
       },
     };
@@ -301,7 +303,9 @@ export function DatasetGatewayWorkspace() {
                     ...(comparison
                       ? ([
                           ["current", "Current"],
-                          ["disagreement", "Ошибки"],
+                          ["candidate", "Patchwork++"],
+                          ["disagreement", "Ошибки Current"],
+                          ["candidate-disagreement", "Ошибки PW++"],
                         ] as const)
                       : []),
                     ["intensity", "Remission"],
@@ -320,20 +324,42 @@ export function DatasetGatewayWorkspace() {
               {comparison ? (
                 <dl className="dataset-preview__metrics">
                   <div>
-                    <dt>Ground IoU</dt>
+                    <dt>Current · IoU</dt>
-                    <dd>{(comparison.metrics.groundIou * 100).toFixed(1)}%</dd>
+                    <dd>{(comparison.metrics.current.groundIou * 100).toFixed(1)}%</dd>
                   </div>
                   <div>
-                    <dt>Precision</dt>
+                    <dt>Current · Recall</dt>
-                    <dd>{(comparison.metrics.precision * 100).toFixed(1)}%</dd>
+                    <dd>{(comparison.metrics.current.recall * 100).toFixed(1)}%</dd>
                   </div>
                   <div>
-                    <dt>Recall</dt>
+                    <dt>Current · Natural</dt>
-                    <dd>{(comparison.metrics.recall * 100).toFixed(1)}%</dd>
+                    <dd>
+                      {(comparison.metrics.current.naturalGroundRecall * 100).toFixed(1)}%
+                    </dd>
                   </div>
                   <div>
-                    <dt>Latency</dt>
+                    <dt>Current · Latency</dt>
-                    <dd>{comparison.latencyMs.toFixed(0)} ms</dd>
+                    <dd>{comparison.latencyMs.current.toFixed(0)} ms</dd>
+                  </div>
+                  <div>
+                    <dt>Patchwork++ · IoU</dt>
+                    <dd>
+                      {(comparison.metrics.patchworkpp.groundIou * 100).toFixed(1)}%
+                    </dd>
+                  </div>
+                  <div>
+                    <dt>Patchwork++ · Recall</dt>
+                    <dd>{(comparison.metrics.patchworkpp.recall * 100).toFixed(1)}%</dd>
+                  </div>
+                  <div>
+                    <dt>Patchwork++ · Natural</dt>
+                    <dd>
+                      {(comparison.metrics.patchworkpp.naturalGroundRecall * 100).toFixed(1)}%
+                    </dd>
+                  </div>
+                  <div>
+                    <dt>Patchwork++ · Latency</dt>
+                    <dd>{comparison.latencyMs.patchworkpp.toFixed(1)} ms</dd>
                   </div>
                 </dl>
               ) : comparisonError ? (
@@ -362,9 +388,19 @@ export function DatasetGatewayWorkspace() {
                         <span><i className="dataset-legend-ground" />current ground</span>
                         <span><i className="dataset-legend-other" />current non-ground</span>
                       </>
+                    ) : previewMode === "candidate" ? (
+                      <>
+                        <span><i className="dataset-legend-ground" />Patchwork++ ground</span>
+                        <span><i className="dataset-legend-other" />Patchwork++ non-ground</span>
+                      </>
                     ) : previewMode === "disagreement" ? (
                       <>
-                        <span><i className="dataset-legend-error" />ошибка относительно labels</span>
+                        <span><i className="dataset-legend-error" />ошибка Current</span>
+                        <span><i className="dataset-legend-other" />совпадение с labels</span>
+                      </>
+                    ) : previewMode === "candidate-disagreement" ? (
+                      <>
+                        <span><i className="dataset-legend-error" />ошибка Patchwork++</span>
                         <span><i className="dataset-legend-other" />совпадение</span>
                       </>
                     ) : (
@@ -380,6 +416,14 @@ export function DatasetGatewayWorkspace() {
                   <p>{previewError ?? "Проверяем point alignment и разметку."}</p>
                 </div>
               )}
+              {comparison ? (
+                <p className="dataset-preview__comparison-note">
+                  Patchwork++ использует опубликованную высоту VLS-128{" "}
+                  {comparison.inputProfile.sensorHeightM.toFixed(2)} м; это
+                  algorithm-specific admission, а не полный vehicle TF и не
+                  normalized scan.
+                </p>
+              ) : null}
             </section>
           ) : null}
 

apps/control-station/src/workspaces/LidarGroundPointCloud.tsx

@@ -7,6 +7,7 @@ export type LidarGroundViewMode =
   | "current"
   | "candidate"
   | "disagreement"
+  | "candidate-disagreement"
   | "semantic"
   | "ground-truth";
 
@@ -21,6 +22,7 @@ export interface LidarGroundPointCloudFrame {
     candidateGround: number[];
     candidateAssigned: number[];
     disagreement: number[];
+    candidateDisagreement?: number[];
     groundTruthGround?: number[];
   };
 }
@@ -119,6 +121,15 @@ function frameColors(
         disagreement ? 0.31 : 0.29,
         disagreement ? 0.22 : 0.35,
       );
+    } else if (mode === "candidate-disagreement") {
+      const disagreement = frame.masks.candidateDisagreement?.[index] === 1;
+      setRgb(
+        colors,
+        offset,
+        disagreement ? 1 : 0.24,
+        disagreement ? 0.31 : 0.29,
+        disagreement ? 0.22 : 0.35,
+      );
     } else {
       setRgb(colors, offset, candidate ? 0.24 : 0.29, candidate ? 0.84 : 0.36, 0.43);
     }

apps/control-station/test/datasetGateway.test.mjs

@@ -194,18 +194,22 @@ test("decodes one bounded point-aligned native-scan preview", async () => {
   );
 });
 
-test("decodes a point-aligned current-vs-ground-truth comparison", async () => {
+test("decodes a point-aligned current-vs-Patchwork++ comparison", async () => {
   const payload = {
-    schema_version: "missioncore.dataset-ground-comparison-preview/v1",
+    schema_version: "missioncore.dataset-ground-comparison-preview/v2",
     source_id: "goose-3d/v2025-08-22",
     frame_id: "frame-1",
     sampling: "deterministic-even-index",
     point_count: 2,
     current_ground: [1, 1],
+    patchwork_ground: [1, 0],
+    patchwork_assigned: [1, 1],
     ground_truth_ground: [1, 0],
     evaluated: [1, 1],
-    disagreement: [0, 1],
+    current_disagreement: [0, 1],
+    patchwork_disagreement: [0, 0],
     metrics: {
+      current: {
         true_positive: 1,
         false_positive: 1,
         false_negative: 0,
@@ -219,27 +223,92 @@ test("decodes a point-aligned current-vs-ground-truth comparison", async () => {
         natural_ground_recall: 0,
         obstacle_non_ground_recall: 1,
       },
-    latency_ms: 12.5,
+      patchworkpp: {
-    provider: {
+        true_positive: 1,
+        false_positive: 0,
+        false_negative: 0,
+        true_negative: 1,
+        precision: 1,
+        recall: 1,
+        f1: 1,
+        ground_iou: 1,
+        accuracy: 1,
+        artificial_ground_recall: 1,
+        natural_ground_recall: 1,
+        obstacle_non_ground_recall: 1,
+      },
+    },
+    latency_ms: {
+      current: 12.5,
+      patchworkpp: 0.4,
+    },
+    patchwork_assigned_fraction: 1,
+    providers: {
+      current: {
         provider_id: "missioncore-local-percentile-ground/v1",
         implementation_sha256: "a".repeat(64),
         ground_truth: false,
       },
+      patchworkpp: {
+        provider_id: "patchworkpp/v1.4.1",
+        source_url: "https://github.com/url-kaist/patchwork-plusplus",
+        source_tag: "v1.4.1",
+        source_commit: "3e6903a1d5537a4cc2ace897b0bbb98a92d6014c",
+        binding_version: "0.0.1",
+        binary_sha256: "b".repeat(64),
+        platform: "linux",
+        machine: "x86_64",
+        ground_truth: false,
+      },
+    },
+    input_profile: {
+      schema_version: "missioncore.goose-patchwork-profile/v1",
+      source_id: "goose-3d/v2025-08-22",
+      representation: "native-scan",
+      sensor_frame: {
+        frame_id: "sensor/lidar/vls128_roof",
+        handedness: "right",
+        x: "forward",
+        y: "left",
+        z: "up",
+        one_revolution: true,
+      },
+      height: {
+        base_link_above_ground_m: 0.64,
+        lidar_above_base_link_m: 1.6,
+        sensor_above_ground_m: 2.24,
+        evidence: "published-dimensioned-schematic",
+      },
+      scope: {
+        patchworkpp_eligible: true,
+        normalized_scan_produced: false,
+        complete_vehicle_transform_known: false,
+        deskew_claimed: false,
+      },
+    },
     safety: {
       qualification_only: true,
       navigation_or_safety_accepted: false,
     },
   };
   const parsed = parseDatasetGroundComparison(payload);
-  assert.equal(parsed.metrics.groundIou, 0.5);
+  assert.equal(parsed.metrics.current.groundIou, 0.5);
-  assert.deepEqual(parsed.disagreement, [0, 1]);
+  assert.equal(parsed.metrics.patchworkpp.groundIou, 1);
+  assert.deepEqual(parsed.patchworkDisagreement, [0, 0]);
+  assert.equal(parsed.inputProfile.sensorHeightM, 2.24);
 
   const fetched = await fetchDatasetGroundComparison({
     fetcher: async () => new Response(JSON.stringify(payload), { status: 200 }),
   });
-  assert.equal(fetched.latencyMs, 12.5);
+  assert.equal(fetched.latencyMs.patchworkpp, 0.4);
 
-  payload.disagreement.pop();
+  payload.patchwork_disagreement.pop();
+  assert.throws(
+    () => parseDatasetGroundComparison(payload),
+    DatasetGatewayContractError,
+  );
+  payload.patchwork_disagreement.push(0);
+  payload.input_profile.height.sensor_above_ground_m = 2.25;
   assert.throws(
     () => parseDatasetGroundComparison(payload),
     DatasetGatewayContractError,

docs/13_LIDAR_WORKER_PRODUCT_AND_ROADMAP.md

@@ -2,7 +2,7 @@
 
 Date: 2026-07-25
 Status: accepted architecture plan; L0/L1 implemented; L2 diagnostic A/B complete;
-Dataset Gateway S0 implemented, first real GOOSE import pending worker D storage
+Dataset Gateway first-frame Current/Patchwork++ A/B complete
 Scope: real scanner records, replay and future live shadow processing
 Explicitly out of scope: Unreal U0/U1, Gaussian assets and simulator rendering
 
@@ -293,7 +293,7 @@ path. K1 manual review or a new real vehicle dataset is reserved for later
 domain adaptation after a public baseline proves that the pipeline and metric
 harness work.
 
-### L2.5 — Dataset Gateway — S0 complete, real import pending
+### L2.5 — Dataset Gateway — first-frame A/B complete
 
 - [x] Define separate `native-scan`, `normalized-scan` and
   `rolling-local-map` representations.
@@ -304,13 +304,16 @@ harness work.
   per-point time and line/ring fields.
 - [x] Require operator-admitted storage under
   `D:\NDC_MISSIONCORE\datasets`.
-- [ ] Configure the worker dataset root and record disk/resource baseline.
+- [x] Configure the worker dataset root and record disk/resource baseline.
-- [ ] Download only the 3.3 GB GOOSE validation archive first and record its
+- [x] Download only the 3.3 GB GOOSE validation archive first and record its
   hash/license/provenance.
-- [ ] Show one real labeled revolution in React with native remission and
+- [x] Show one real labeled revolution in React with native remission and
   ground-truth superclass coloring.
-- [ ] Admit an explicit frame/mounting profile before normalization.
+- [x] Admit the published Patchwork-specific axes/height profile without
-- [ ] Run current ground and Patchwork++ against GOOSE ground truth.
+  claiming full vehicle TF, deskew or a general normalized scan.
+- [x] Run current ground and Patchwork++ against the same GOOSE native scan and
+  ground truth; keep the result one-frame diagnostic.
+- [ ] Expand Current/Patchwork++ qualification to the validation split.
 - [ ] Add named range/FOV/density/noise/dropout degradation profiles without
   overwriting the native frame.
 
@@ -387,8 +390,8 @@ The near-term value is not a prettier point cloud:
 - hardware selection becomes evidence-driven: a future vehicle LiDAR is
   accepted by its timing/fields/profile, not by vendor marketing.
 
-The highest-value immediate work is the worker-side GOOSE validation import,
+The highest-value immediate work is now the validation-split
-the first labeled native scan in React and an accuracy-bearing ground A/B.
+Current/Patchwork++ gate plus named range/FOV/density/noise/dropout degradation
-LiDAR-native detection follows on the same gateway. Nvblox and alternative
+profiles. LiDAR-native detection follows on the same gateway. Nvblox and
-SLAM remain later because their timing, pose and scan-geometry gates are not yet
+alternative SLAM remain later because their timing, pose and scan-geometry
-satisfied.
+gates are not yet satisfied.

docs/14_LIDAR_DATASET_GATEWAY.md

@@ -39,6 +39,12 @@ Implemented now:
   `Открыть` action;
 - one admitted GOOSE validation frame with source colors, normalized remission
   and an independent ground-truth view;
+- a published, dimensioned MuCAR-3/VLS-128 Patchwork++ input profile:
+  sensor frame `x-forward / y-left / z-up`, physical height `2.24 m`, native
+  one-revolution scan and explicit absence of deskew/full vehicle TF claims;
+- a reproducible Current/Patchwork++ A/B artifact with point-aligned masks,
+  disagreement views, independent-label metrics, latency and exact provider
+  identities;
 - a separate **Парк → Диагностика LiDAR** surface containing only real sensor
   recordings and their operational evidence.
 
@@ -152,11 +158,13 @@ time; TTL/dynamic filtering prevents stale ghosts.
 4. [Done] Import one labeled frame and expose it in React as `native-scan`.
 5. [Done] Show native remission, the original semantic palette and
    ground-truth superclass coloring.
-6. Add a declared GOOSE frame/mounting profile and produce `normalized-scan`.
+6. [Patchwork-specific profile done; general normalization still pending] Admit
-7. [Current baseline done; Patchwork++ blocked on mounting evidence] Run the
+   the published VLS-128 axes and physical height without claiming a complete
-   current ground heuristic and Patchwork++ against independent labels.
+   numeric vehicle TF or deskewed `normalized-scan`.
+7. [One-frame A/B done] Run the current ground heuristic and pinned
+   Patchwork++ against the same native scan and independent labels.
 8. Add sensor-degradation profiles for range, FOV, density, noise and dropout.
-9. Only after the one-frame contract passes, expand to the validation split and
+9. Expand the A/B to the validation split; only after that qualification gate,
    add a rolling-map sequence with localization evidence.
 
 ## Acceptance checklist
@@ -172,9 +180,11 @@ time; TTL/dynamic filtering prevents stale ghosts.
 - [x] Worker D root configured.
 - [x] GOOSE validation archive hash recorded.
 - [x] First real labeled frame visible in React.
-- [ ] Coordinate and mounting profile admitted.
+- [x] Patchwork-specific axes and physical-height profile admitted.
+- [ ] Complete vehicle transform and general `normalized-scan` admitted.
 - [x] Current local-percentile baseline measured against ground truth.
-- [ ] Patchwork++ accuracy measured against ground truth.
+- [x] Patchwork++ one-frame accuracy measured against ground truth.
+- [ ] Current/Patchwork++ validation-split gate qualified.
 - [ ] Sensor-degradation matrix qualified.
 - [ ] Rolling local map with pose/TTL/dynamic policy qualified.
 
@@ -229,10 +239,54 @@ previous exact-radius result. This removes the previous all-points scan for
 every occupied cell, but the measured latency still classifies it as a
 diagnostic baseline rather than an onboard candidate.
 
-Patchwork++ is intentionally not scored yet. The validation ZIP contains XYZI,
+## First Patchwork++ A/B result
-labels, mapping, LICENSE and CHANGELOG but no numeric TF/mounting calibration.
+
-GOOSE documents the VLS-128 as a roof LiDAR and publishes a separate MuCAR-3 TF
+GOOSE's dimensioned MuCAR-3 schematic provides the two vertical dimensions
-tree, but the graph image alone is not physical-height evidence. The next gate
+needed by this algorithm-specific gate: `base_link` is `0.64 m` above ground
-is to admit the numeric transform from `base_link_ground` to
+and the VLS-128 optical center is `1.60 m` above `base_link`. The admitted
-`sensor/lidar/vls128_roof`, declare the source axis convention, and only then
+Patchwork++ height is therefore `2.24 m`. The same published schematic declares
-run Patchwork++.
+the sensor axes as `x` forward, `y` left and `z` up. An independent fit to
+GOOSE-labeled near-field ground observed a `-2.18 .. -2.14 m` intercept; this
+was a non-calibrating cross-check, not the source of the height.
+
+This narrowly admits the input required by Patchwork++ on the native
+sensor-centric scan. It does **not** claim a complete numeric vehicle transform,
+per-point timing, deskew or a general `normalized-scan`.
+
+Official Patchwork++ `v1.4.1`, source commit
+`3e6903a1d5537a4cc2ace897b0bbb98a92d6014c`, was run against the same first
+frame and independent labels as Current:
+
+| Metric | Current | Patchwork++ |
+| --- | ---: | ---: |
+| Ground IoU | `49.6165%` | `60.3702%` |
+| Precision | `73.5892%` | `72.6612%` |
+| Recall | `60.3659%` | `78.1130%` |
+| F1 | `66.3249%` | `75.2886%` |
+| Artificial-ground recall | `95.2364%` | `98.9688%` |
+| Natural-ground recall | `49.4516%` | `71.5853%` |
+| Obstacle non-ground recall | `79.6145%` | `92.1729%` |
+| Worker latency | `3966.53 ms` | `15.89 ms` |
+
+Reproducibility pins:
+
+- benchmark identity:
+  `2a6d05f54a9e2ac727d9c850c1133f2fb539bfeddbd97c07cfd198ccb239162c`;
+- full point-aligned prediction SHA-256:
+  `190f455c6e47911921b7f6454913e1bb2d0b8b5814ee2e34d7b63295afddc7ef`;
+- bounded browser preview SHA-256:
+  `e23ca573103faf931523415e6c263248737eb6600f482b63de685b725bbc28c3`;
+- Patchwork++ binary SHA-256:
+  `be8038b2098c83fe53841aa8ae19e362910e9056fe0ee7b9304c1ee5c5941094`.
+
+Patchwork++ wins this frame by `10.75` percentage points of Ground IoU, raises
+natural-ground recall by `22.13` points and is roughly `250x` faster in this
+run. The result is deliberately `one-frame-diagnostic`: it makes Patchwork++
+the candidate for validation-split and degradation qualification, not an
+accepted navigation or safety provider.
+
+Primary source evidence:
+
+- [GOOSE MuCAR-3 sensor setup](https://goose-dataset.de/docs/mucar3/);
+- [GOOSE paper, Figure 3](https://arxiv.org/pdf/2310.16788);
+- [Patchwork++ v1.4.1](https://github.com/url-kaist/patchwork-plusplus/tree/v1.4.1).

docs/adr/0021-dataset-gateway-representation-boundary.md

@@ -87,24 +87,34 @@ unavailable rather than inventing timestamps.
 - Sensor adaptation may change range, FOV, point density, noise and dropout for
   robustness experiments, but it cannot recreate lost timestamps, occlusions
   or material response.
-- Patchwork++ becomes eligible for a real quality gate only on a sensor-centric
+- Patchwork++ becomes eligible for an algorithm-specific quality gate on a
-  scan with declared scan geometry and physical mounting height plus
+  sensor-centric scan with declared axes, physical mounting height and
-  independent labels.
+  independent labels. That narrow admission does not itself create a general
+  `normalized-scan`.
 - Stable operator visualization is owned by rolling-map policy, not by the
   ground classifier.
 - Public labels may qualify an algorithm independently of the production
   sensor. The first GOOSE frame exposed a `49.62%` Ground IoU and only `49.45%`
   natural-ground recall for the current local-percentile baseline; these are
   diagnostic results, not production promotion.
-- A dataset label contract does not imply a mounting contract. Patchwork++
+- A dataset label contract does not imply a mounting contract. For MuCAR-3,
-  remains blocked until the numeric GOOSE roof-LiDAR transform and physical
+  GOOSE's published dimensioned schematic admits the Patchwork-specific
-  height are admitted from source evidence.
+  `2.24 m` sensor height (`0.64 + 1.60 m`) and `x-forward / y-left / z-up`
+  axes. The complete numeric vehicle transform, deskew and general
+  `normalized-scan` remain unavailable.
+- On the first independently labeled frame, pinned Patchwork++ `v1.4.1`
+  achieved `60.37%` Ground IoU, `71.59%` natural-ground recall and `15.89 ms`
+  worker latency versus Current's `49.62%`, `49.45%` and `3966.53 ms`.
+  This is a one-frame diagnostic candidate decision, not production promotion.
 
 ## Primary references
 
 - [GOOSE dataset structure](https://goose-dataset.de/docs/dataset-structure/)
 - [GOOSE setup and archive sizes](https://goose-dataset.de/docs/setup/)
 - [GOOSE 3D challenge ontology](https://goose-dataset.de/docs/3d-semantic-segmentation-challenge/)
+- [GOOSE MuCAR-3 sensor setup](https://goose-dataset.de/docs/mucar3/)
+- [GOOSE paper and dimensioned sensor schematic](https://arxiv.org/pdf/2310.16788)
+- [Patchwork++ v1.4.1](https://github.com/url-kaist/patchwork-plusplus/tree/v1.4.1)
 - [Livox ROS Driver 2 point formats](https://github.com/Livox-SDK/livox_ros_driver2)
 - [Livox LIO motion-distortion handling](https://github.com/Livox-SDK/LIO-Livox)
 - [ROS FilterDeskew timestamp requirement](https://docs.ros.org/en/noetic/api/mp2p_icp/html/classmp2p__icp__filters_1_1FilterDeskew.html)

src/k1link/compute/lidar_ground.py

@@ -1,18 +1,14 @@
 from __future__ import annotations
 
 import hashlib
-import importlib
 import json
 import math
 import os
-import platform
 import re
 import shutil
-import time
 from collections.abc import Mapping
 from datetime import UTC, datetime
 from pathlib import Path
-from types import ModuleType
 from typing import Any, Final
 
 import numpy as np
@@ -25,9 +21,21 @@ from k1link.ground_segmentation import (
     GroundSegmenter,
     LocalPercentileGroundSegmenter,
 )
+from k1link.ground_segmentation import (
+    PATCHWORKPP_SOURCE_COMMIT as PATCHWORKPP_SOURCE_COMMIT,
+)
+from k1link.ground_segmentation import (
+    PATCHWORKPP_SOURCE_TAG as PATCHWORKPP_SOURCE_TAG,
+)
+from k1link.ground_segmentation import (
+    PATCHWORKPP_SOURCE_URL as PATCHWORKPP_SOURCE_URL,
+)
 from k1link.ground_segmentation import (
     GroundSegmentationError as LidarGroundError,
 )
+from k1link.ground_segmentation import (
+    PatchworkPPGroundSegmenter as PatchworkPPGroundSegmenter,
+)
 
 from .lidar_contract import LidarContractError, sensor_frame_xyzi
 from .lidar_replay import LidarReplayPackV2
@@ -42,107 +50,9 @@ LIDAR_GROUND_MANIFEST_NAME: Final = "manifest.json"
 LIDAR_GROUND_ANNOTATION_LABELS_NAME: Final = "labels-template.npz"
 MAX_GROUND_FRAME_POINTS: Final = 200_000
 
-PATCHWORKPP_SOURCE_URL: Final = "https://github.com/url-kaist/patchwork-plusplus"
-PATCHWORKPP_SOURCE_TAG: Final = "v1.4.1"
-PATCHWORKPP_SOURCE_COMMIT: Final = "3e6903a1d5537a4cc2ace897b0bbb98a92d6014c"
-
 _BENCHMARK_ID = re.compile(r"^ground-benchmark-[a-f0-9]{64}$")
 _ANNOTATION_TEMPLATE_ID = re.compile(r"^ground-annotation-template-[a-f0-9]{64}$")
 _SHA256 = re.compile(r"^[a-f0-9]{64}$")
-_GIT_SHA1 = re.compile(r"^[a-f0-9]{40}$")
-
-
-class PatchworkPPGroundSegmenter:
-    """Runtime-only adapter for the pinned official Patchwork++ Python binding."""
-
-    def __init__(
-        self,
-        module: ModuleType,
-        profile: GroundBenchmarkProfile,
-        *,
-        source_commit: str = PATCHWORKPP_SOURCE_COMMIT,
-        source_tag: str = PATCHWORKPP_SOURCE_TAG,
-    ) -> None:
-        if _GIT_SHA1.fullmatch(source_commit) is None:
-            raise LidarGroundError("Patchwork++ source commit is invalid")
-        if source_tag != PATCHWORKPP_SOURCE_TAG:
-            raise LidarGroundError("Patchwork++ source tag is not admitted")
-        module_path_value = getattr(module, "__file__", None)
-        if not isinstance(module_path_value, str):
-            raise LidarGroundError("Patchwork++ module has no verifiable binary")
-        module_path = Path(module_path_value).resolve(strict=True)
-        params = module.Parameters()
-        params.sensor_height = profile.patchwork_sensor_height_proxy_m
-        params.min_range = profile.patchwork_minimum_range_m
-        params.max_range = profile.patchwork_maximum_range_m
-        params.enable_RNR = True
-        params.enable_RVPF = True
-        params.enable_TGR = True
-        params.verbose = False
-        self._estimator = module.patchworkpp(params)
-        self._identity = {
-            "provider_id": "patchworkpp/v1.4.1",
-            "source_url": PATCHWORKPP_SOURCE_URL,
-            "source_tag": source_tag,
-            "source_commit": source_commit,
-            "binding_version": str(getattr(module, "__version__", "unknown")),
-            "binary_sha256": _sha256(module_path),
-            "platform": platform.system().lower(),
-            "machine": platform.machine().lower(),
-            "ground_truth": False,
-        }
-
-    @classmethod
-    def load(
-        cls,
-        profile: GroundBenchmarkProfile = DEFAULT_GROUND_BENCHMARK_PROFILE,
-        *,
-        module_name: str = "pypatchworkpp",
-        source_commit: str = PATCHWORKPP_SOURCE_COMMIT,
-        source_tag: str = PATCHWORKPP_SOURCE_TAG,
-    ) -> PatchworkPPGroundSegmenter:
-        try:
-            module = importlib.import_module(module_name)
-        except ImportError as exc:
-            raise LidarGroundError("Pinned Patchwork++ Python binding is unavailable") from exc
-        return cls(
-            module,
-            profile,
-            source_commit=source_commit,
-            source_tag=source_tag,
-        )
-
-    @property
-    def identity(self) -> Mapping[str, object]:
-        return self._identity
-
-    def segment(self, xyzi: npt.NDArray[np.float32]) -> GroundSegmentation:
-        points = np.ascontiguousarray(_xyzi(xyzi), dtype=np.float32)
-        started = time.perf_counter_ns()
-        self._estimator.estimateGround(points)
-        latency_ms = (time.perf_counter_ns() - started) / 1_000_000
-        ground_indices = np.asarray(
-            self._estimator.getGroundIndices(),
-            dtype=np.int64,
-        ).reshape((-1,))
-        nonground_indices = np.asarray(
-            self._estimator.getNongroundIndices(),
-            dtype=np.int64,
-        ).reshape((-1,))
-        _indices(ground_indices, points.shape[0], "Patchwork++ ground")
-        _indices(nonground_indices, points.shape[0], "Patchwork++ non-ground")
-        if np.intersect1d(ground_indices, nonground_indices).size:
-            raise LidarGroundError("Patchwork++ assigned one point twice")
-        ground = np.zeros(points.shape[0], dtype=np.bool_)
-        assigned = np.zeros(points.shape[0], dtype=np.bool_)
-        ground[ground_indices] = True
-        assigned[ground_indices] = True
-        assigned[nonground_indices] = True
-        return GroundSegmentation(
-            ground_mask=ground,
-            assigned_mask=assigned,
-            latency_ms=latency_ms,
-        )
 
 
 class LidarGroundBenchmarkV1:

src/k1link/datasets/__init__.py

@@ -15,9 +15,17 @@ from k1link.datasets.gateway import (
     read_semantic_kitti_frame,
 )
 from k1link.datasets.goose_benchmark import (
+    GOOSE_GROUND_AB_BENCHMARK_SCHEMA,
+    GOOSE_GROUND_AB_PREVIEW_SCHEMA,
     GOOSE_GROUND_BENCHMARK_SCHEMA,
     GOOSE_GROUND_PREVIEW_SCHEMA,
     benchmark_goose_current_ground,
+    benchmark_goose_patchwork_ground,
+)
+from k1link.datasets.goose_profile import (
+    DEFAULT_GOOSE_PATCHWORK_PROFILE,
+    GOOSE_PATCHWORK_PROFILE_SCHEMA,
+    GoosePatchworkProfile,
 )
 
 __all__ = [
@@ -25,9 +33,15 @@ __all__ = [
     "DatasetAdmissionError",
     "DatasetFrameError",
     "DatasetPointFrame",
+    "DEFAULT_GOOSE_PATCHWORK_PROFILE",
+    "GOOSE_GROUND_AB_BENCHMARK_SCHEMA",
+    "GOOSE_GROUND_AB_PREVIEW_SCHEMA",
     "GOOSE_GROUND_BENCHMARK_SCHEMA",
     "GOOSE_GROUND_PREVIEW_SCHEMA",
+    "GOOSE_PATCHWORK_PROFILE_SCHEMA",
+    "GoosePatchworkProfile",
     "benchmark_goose_current_ground",
+    "benchmark_goose_patchwork_ground",
     "configured_dataset_admission_manifest",
     "configured_dataset_ground_preview",
     "configured_dataset_preview",

src/k1link/datasets/cli.py

@@ -9,7 +9,10 @@ from typing import Annotated
 import typer
 
 from k1link.datasets.goose_admission import GooseAdmissionError, admit_goose_validation
-from k1link.datasets.goose_benchmark import benchmark_goose_current_ground
+from k1link.datasets.goose_benchmark import (
+    benchmark_goose_current_ground,
+    benchmark_goose_patchwork_ground,
+)
 
 app = typer.Typer(
     add_completion=False,
@@ -63,5 +66,22 @@ def benchmark_goose_current_ground_command(
     typer.echo(json.dumps(report, ensure_ascii=False, sort_keys=True))
 
 
+@app.command("benchmark-goose-patchwork-ground")
+def benchmark_goose_patchwork_ground_command(
+    dataset_root: Annotated[
+        Path,
+        typer.Option("--dataset-root", exists=True, file_okay=False, resolve_path=True),
+    ],
+) -> None:
+    """Compare current and pinned Patchwork++ providers against GOOSE labels."""
+
+    try:
+        report = benchmark_goose_patchwork_ground(dataset_root)
+    except (GooseAdmissionError, ValueError) as exc:
+        typer.echo(str(exc), err=True)
+        raise typer.Exit(code=2) from exc
+    typer.echo(json.dumps(report, ensure_ascii=False, sort_keys=True))
+
+
 if __name__ == "__main__":
     app()

src/k1link/datasets/gateway.py

@@ -16,10 +16,12 @@ from typing import Any, Final, Literal
 import numpy as np
 import numpy.typing as npt
 
+from k1link.datasets.goose_profile import DEFAULT_GOOSE_PATCHWORK_PROFILE
+
 DATASET_GATEWAY_CATALOG_SCHEMA: Final = "missioncore.dataset-gateway-catalog/v2"
 DATASET_ADMISSION_SCHEMA: Final = "missioncore.dataset-admission/v1"
 DATASET_PREVIEW_SCHEMA: Final = "missioncore.dataset-native-scan-preview/v1"
-DATASET_GROUND_PREVIEW_SCHEMA: Final = "missioncore.dataset-ground-comparison-preview/v1"
+DATASET_GROUND_PREVIEW_SCHEMA: Final = "missioncore.dataset-ground-comparison-preview/v2"
 DATASET_ROOT_ENV: Final = "MISSIONCORE_DATASET_ROOT"
 DATASET_ADMISSION_MANIFEST_ENV: Final = "MISSIONCORE_DATASET_ADMISSION_MANIFEST"
 DATASET_PREVIEW_ENV: Final = "MISSIONCORE_DATASET_PREVIEW"
@@ -316,8 +318,7 @@ def read_dataset_admission_manifest(path: Path) -> dict[str, Any]:
         raise DatasetAdmissionError("dataset admission status is incompatible")
     storage = _object(document["storage"], "storage")
     if (
-        set(storage)
+        set(storage) != {"policy", "admitted", "canonical_root", "path_exposed"}
-        != {"policy", "admitted", "canonical_root", "path_exposed"}
         or storage.get("policy") != "worker-d-only"
         or storage.get("admitted") is not True
         or storage.get("canonical_root") is not True
@@ -338,8 +339,7 @@ def read_dataset_admission_manifest(path: Path) -> dict[str, Any]:
             "vendor_checksum_available",
         }
         or archive.get("filename") != "goose_3d_val.zip"
-        or archive.get("source_url")
+        or archive.get("source_url") != "https://goose-dataset.de/storage/goose_3d_val.zip"
-        != "https://goose-dataset.de/storage/goose_3d_val.zip"
         or archive.get("vendor_checksum_available") is not False
     ):
         raise DatasetAdmissionError("dataset archive admission is incompatible")
@@ -385,9 +385,7 @@ def read_dataset_native_scan_preview(path: Path) -> dict[str, Any]:
     ):
         raise DatasetAdmissionError("dataset preview identity is incompatible")
     point_count = _positive_integer(document.get("point_count"), "point_count")
-    source_point_count = _positive_integer(
+    source_point_count = _positive_integer(document.get("source_point_count"), "source_point_count")
-        document.get("source_point_count"), "source_point_count"
-    )
     if point_count > 50_000 or point_count > source_point_count:
         raise DatasetAdmissionError("dataset preview point count is incompatible")
     points = document.get("points_xyz_m")
@@ -456,20 +454,28 @@ def read_dataset_ground_preview(path: Path) -> dict[str, Any]:
     point_count = _positive_integer(document.get("point_count"), "point_count")
     if point_count > 50_000:
         raise DatasetAdmissionError("dataset ground preview point count is incompatible")
-    for key in ("current_ground", "ground_truth_ground", "evaluated", "disagreement"):
+    for key in (
+        "current_ground",
+        "patchwork_ground",
+        "patchwork_assigned",
+        "ground_truth_ground",
+        "evaluated",
+        "current_disagreement",
+        "patchwork_disagreement",
+    ):
         values = document.get(key)
         if (
             not isinstance(values, list)
             or len(values) != point_count
             or any(
-                not isinstance(value, int)
+                not isinstance(value, int) or isinstance(value, bool) or value not in (0, 1)
-                or isinstance(value, bool)
-                or value not in (0, 1)
                 for value in values
             )
         ):
             raise DatasetAdmissionError(f"dataset ground preview {key} is incompatible")
-    metrics = _object(document.get("metrics"), "metrics")
+    metrics_by_provider = _object(document.get("metrics"), "metrics")
+    if set(metrics_by_provider) != {"current", "patchworkpp"}:
+        raise DatasetAdmissionError("dataset ground provider metrics are incompatible")
     expected_metrics = {
         "true_positive",
         "false_positive",
@@ -484,40 +490,88 @@ def read_dataset_ground_preview(path: Path) -> dict[str, Any]:
         "natural_ground_recall",
         "obstacle_non_ground_recall",
     }
+    for provider_id in ("current", "patchworkpp"):
+        metrics = _object(metrics_by_provider[provider_id], f"metrics.{provider_id}")
         if set(metrics) != expected_metrics:
             raise DatasetAdmissionError("dataset ground metrics are incompatible")
         for key, value in metrics.items():
             if key in {"true_positive", "false_positive", "false_negative", "true_negative"}:
-            _nonnegative_integer(value, f"metrics.{key}")
+                _nonnegative_integer(value, f"metrics.{provider_id}.{key}")
             elif (
                 not isinstance(value, (int, float))
                 or isinstance(value, bool)
                 or not np.isfinite(value)
                 or not 0 <= value <= 1
             ):
-            raise DatasetAdmissionError(f"dataset ground metric {key} is incompatible")
+                raise DatasetAdmissionError(
-    latency = document.get("latency_ms")
+                    f"dataset ground metric {provider_id}.{key} is incompatible"
+                )
+    latency = _object(document.get("latency_ms"), "latency_ms")
+    if set(latency) != {"current", "patchworkpp"}:
+        raise DatasetAdmissionError("dataset ground latency providers are incompatible")
+    for provider_id, value in latency.items():
         if (
-        not isinstance(latency, (int, float))
+            not isinstance(value, (int, float))
-        or isinstance(latency, bool)
+            or isinstance(value, bool)
-        or not np.isfinite(latency)
+            or not np.isfinite(value)
-        or latency < 0
+            or value < 0
         ):
-        raise DatasetAdmissionError("dataset ground latency is incompatible")
+            raise DatasetAdmissionError(f"dataset ground latency {provider_id} is incompatible")
-    provider = _object(document.get("provider"), "provider")
+    providers = _object(document.get("providers"), "providers")
+    if set(providers) != {"current", "patchworkpp"}:
+        raise DatasetAdmissionError("dataset ground providers are incompatible")
+    current_provider = _object(providers["current"], "providers.current")
     if (
-        set(provider) != {"provider_id", "implementation_sha256", "ground_truth"}
+        set(current_provider) != {"provider_id", "implementation_sha256", "ground_truth"}
-        or provider.get("provider_id") != "missioncore-local-percentile-ground/v1"
+        or current_provider.get("provider_id") != "missioncore-local-percentile-ground/v1"
-        or provider.get("ground_truth") is not False
+        or current_provider.get("ground_truth") is not False
     ):
         raise DatasetAdmissionError("dataset ground provider is incompatible")
-    digest = provider.get("implementation_sha256")
+    digest = current_provider.get("implementation_sha256")
     if (
         not isinstance(digest, str)
         or len(digest) != 64
         or any(character not in "0123456789abcdef" for character in digest)
     ):
         raise DatasetAdmissionError("dataset ground provider digest is incompatible")
+    patchwork_provider = _object(providers["patchworkpp"], "providers.patchworkpp")
+    if (
+        set(patchwork_provider)
+        != {
+            "provider_id",
+            "source_url",
+            "source_tag",
+            "source_commit",
+            "binding_version",
+            "binary_sha256",
+            "platform",
+            "machine",
+            "ground_truth",
+        }
+        or patchwork_provider.get("provider_id") != "patchworkpp/v1.4.1"
+        or patchwork_provider.get("source_tag") != "v1.4.1"
+        or patchwork_provider.get("source_commit") != "3e6903a1d5537a4cc2ace897b0bbb98a92d6014c"
+        or patchwork_provider.get("ground_truth") is not False
+    ):
+        raise DatasetAdmissionError("dataset Patchwork++ provider is incompatible")
+    binary_digest = patchwork_provider.get("binary_sha256")
+    if (
+        not isinstance(binary_digest, str)
+        or len(binary_digest) != 64
+        or any(character not in "0123456789abcdef" for character in binary_digest)
+    ):
+        raise DatasetAdmissionError("dataset Patchwork++ binary digest is incompatible")
+    assigned_fraction = document.get("patchwork_assigned_fraction")
+    if (
+        not isinstance(assigned_fraction, (int, float))
+        or isinstance(assigned_fraction, bool)
+        or not np.isfinite(assigned_fraction)
+        or not 0 <= assigned_fraction <= 1
+    ):
+        raise DatasetAdmissionError("dataset Patchwork++ assigned fraction is incompatible")
+    input_profile = _object(document.get("input_profile"), "input_profile")
+    if input_profile != DEFAULT_GOOSE_PATCHWORK_PROFILE.to_dict():
+        raise DatasetAdmissionError("dataset Patchwork++ input profile is incompatible")
     safety = _object(document.get("safety"), "safety")
     if safety != {
         "qualification_only": True,

src/k1link/datasets/goose_benchmark.py

@@ -18,14 +18,22 @@ from k1link.datasets.goose_admission import (
     GooseAdmissionError,
     read_goose_label_mapping,
 )
+from k1link.datasets.goose_profile import (
+    DEFAULT_GOOSE_PATCHWORK_PROFILE,
+    GoosePatchworkProfile,
+)
 from k1link.ground_segmentation import (
     DEFAULT_GROUND_BENCHMARK_PROFILE,
     GroundBenchmarkProfile,
+    GroundSegmenter,
     LocalPercentileGroundSegmenter,
+    PatchworkPPGroundSegmenter,
 )
 
 GOOSE_GROUND_BENCHMARK_SCHEMA: Final = "missioncore.goose-ground-benchmark/v1"
 GOOSE_GROUND_PREVIEW_SCHEMA: Final = "missioncore.dataset-ground-comparison-preview/v1"
+GOOSE_GROUND_AB_BENCHMARK_SCHEMA: Final = "missioncore.goose-ground-ab-benchmark/v1"
+GOOSE_GROUND_AB_PREVIEW_SCHEMA: Final = "missioncore.dataset-ground-comparison-preview/v2"
 MAX_PREVIEW_POINTS: Final = 50_000
 
 
@@ -135,8 +143,7 @@ def benchmark_goose_current_ground(
         "ground_truth_ground": ground_truth[indices].astype(np.uint8).tolist(),
         "evaluated": evaluated[indices].astype(np.uint8).tolist(),
         "disagreement": (
-            evaluated[indices]
+            evaluated[indices] & (prediction.ground_mask[indices] != ground_truth[indices])
-            & (prediction.ground_mask[indices] != ground_truth[indices])
         )
         .astype(np.uint8)
         .tolist(),
@@ -179,6 +186,204 @@ def benchmark_goose_current_ground(
     return report
 
 
+def benchmark_goose_patchwork_ground(
+    dataset_root: Path,
+    *,
+    current_profile: GroundBenchmarkProfile = DEFAULT_GROUND_BENCHMARK_PROFILE,
+    patchwork_profile: GoosePatchworkProfile = DEFAULT_GOOSE_PATCHWORK_PROFILE,
+    patchwork: GroundSegmenter | None = None,
+    patchwork_module_name: str = "pypatchworkpp",
+    preview_points: int = MAX_PREVIEW_POINTS,
+) -> dict[str, Any]:
+    """Score current and pinned Patchwork++ providers against one GOOSE frame."""
+
+    root = dataset_root.expanduser().absolute()
+    if not _is_worker_dataset_root(root):
+        raise GooseAdmissionError("GOOSE benchmark requires the canonical worker D root")
+    manifest = read_dataset_admission_manifest(root / "state/goose-3d-v2025-08-22.json")
+    if manifest["status"] != "frame-ready":
+        raise GooseAdmissionError("GOOSE frame is not admitted for benchmarking")
+    archive_sha256 = str(manifest["archive"]["sha256"])
+    frame_id = str(manifest["frame"]["frame_id"])
+    install = root / "goose-3d/v2025-08-22/installs" / archive_sha256
+    frame_root = install / "frames" / frame_id
+    frame = read_semantic_kitti_frame(
+        frame_root / "points.bin",
+        frame_root / "labels.label",
+    )
+    labels = read_goose_label_mapping(install / "goose_label_mapping.csv")
+    categories = np.asarray(
+        [labels[int(value)]["challenge_category_id"] for value in frame.semantic_labels],
+        dtype=np.uint8,
+    )
+    evaluated = categories != 0
+    ground_truth = (categories == 2) | (categories == 3)
+    xyzi = np.column_stack((frame.points_xyz_m, frame.remission)).astype(
+        np.float32,
+        copy=False,
+    )
+    current = LocalPercentileGroundSegmenter(current_profile)
+    candidate = (
+        patchwork
+        if patchwork is not None
+        else PatchworkPPGroundSegmenter.load(
+            patchwork_profile,
+            module_name=patchwork_module_name,
+        )
+    )
+    current_result = current.segment(xyzi)
+    candidate_result = candidate.segment(xyzi)
+    for result, label in (
+        (current_result, "current"),
+        (candidate_result, "Patchwork++"),
+    ):
+        if result.ground_mask.shape != (frame.point_count,) or result.assigned_mask.shape != (
+            frame.point_count,
+        ):
+            raise GooseAdmissionError(f"{label} result is not point-aligned")
+    current_metrics = _ground_metrics(
+        current_result.ground_mask,
+        ground_truth,
+        evaluated,
+        categories,
+    )
+    candidate_metrics = _ground_metrics(
+        candidate_result.ground_mask,
+        ground_truth,
+        evaluated,
+        categories,
+    )
+    candidate_assigned_fraction = float(np.mean(candidate_result.assigned_mask))
+    profile_document = patchwork_profile.to_dict()
+    current_profile_document = _goose_current_profile_document(current_profile)
+    identity_document = {
+        "source_id": GOOSE_SOURCE_ID,
+        "archive_sha256": archive_sha256,
+        "frame_id": frame_id,
+        "source_point_count": frame.point_count,
+        "input_profile": profile_document,
+        "current_profile": current_profile_document,
+        "providers": {
+            "current": dict(current.identity),
+            "patchworkpp": dict(candidate.identity),
+        },
+    }
+    identity_sha256 = _canonical_sha256(identity_document)
+    output_root = install / "benchmarks" / f"ground-ab-{identity_sha256}"
+    output_root.mkdir(parents=True, exist_ok=True)
+    report_path = output_root / "report.json"
+    if report_path.is_file():
+        try:
+            existing = json.loads(report_path.read_text(encoding="utf-8"))
+        except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc:
+            raise GooseAdmissionError("existing GOOSE A/B report is invalid") from exc
+        if (
+            not isinstance(existing, dict)
+            or existing.get("schema_version") != GOOSE_GROUND_AB_BENCHMARK_SCHEMA
+            or existing.get("identity_sha256") != identity_sha256
+            or not (output_root / "prediction.npz").is_file()
+            or not (output_root / "preview.json").is_file()
+        ):
+            raise GooseAdmissionError("existing GOOSE A/B benchmark is incomplete")
+        return existing
+
+    prediction_path = output_root / "prediction.npz"
+    _atomic_npz(
+        prediction_path,
+        current_ground=current_result.ground_mask.astype(np.uint8),
+        patchwork_ground=candidate_result.ground_mask.astype(np.uint8),
+        patchwork_assigned=candidate_result.assigned_mask.astype(np.uint8),
+        ground_truth_ground=ground_truth.astype(np.uint8),
+        evaluated=evaluated.astype(np.uint8),
+    )
+    sample_count = min(frame.point_count, preview_points)
+    indices = np.linspace(0, frame.point_count - 1, sample_count, dtype=np.int64)
+    current_disagreement = evaluated & (current_result.ground_mask != ground_truth)
+    candidate_disagreement = evaluated & (candidate_result.ground_mask != ground_truth)
+    preview = {
+        "schema_version": GOOSE_GROUND_AB_PREVIEW_SCHEMA,
+        "source_id": GOOSE_SOURCE_ID,
+        "frame_id": frame_id,
+        "sampling": "deterministic-even-index",
+        "point_count": sample_count,
+        "current_ground": current_result.ground_mask[indices].astype(np.uint8).tolist(),
+        "patchwork_ground": candidate_result.ground_mask[indices].astype(np.uint8).tolist(),
+        "patchwork_assigned": (candidate_result.assigned_mask[indices].astype(np.uint8).tolist()),
+        "ground_truth_ground": ground_truth[indices].astype(np.uint8).tolist(),
+        "evaluated": evaluated[indices].astype(np.uint8).tolist(),
+        "current_disagreement": (current_disagreement[indices].astype(np.uint8).tolist()),
+        "patchwork_disagreement": (candidate_disagreement[indices].astype(np.uint8).tolist()),
+        "metrics": {
+            "current": current_metrics,
+            "patchworkpp": candidate_metrics,
+        },
+        "latency_ms": {
+            "current": current_result.latency_ms,
+            "patchworkpp": candidate_result.latency_ms,
+        },
+        "patchwork_assigned_fraction": candidate_assigned_fraction,
+        "providers": {
+            "current": dict(current.identity),
+            "patchworkpp": dict(candidate.identity),
+        },
+        "input_profile": profile_document,
+        "safety": {
+            "qualification_only": True,
+            "navigation_or_safety_accepted": False,
+        },
+    }
+    preview_path = output_root / "preview.json"
+    _atomic_json(preview_path, preview)
+    winner = (
+        "patchworkpp"
+        if candidate_metrics["ground_iou"] > current_metrics["ground_iou"]
+        else "current"
+        if current_metrics["ground_iou"] > candidate_metrics["ground_iou"]
+        else "tie"
+    )
+    report = {
+        "schema_version": GOOSE_GROUND_AB_BENCHMARK_SCHEMA,
+        "identity_sha256": identity_sha256,
+        "identity": identity_document,
+        "ground_truth": {
+            "source": "GOOSE point-wise semantic labels",
+            "ground_categories": ["artificial_ground", "natural_ground"],
+            "void_excluded": True,
+            "evaluated_points": int(np.count_nonzero(evaluated)),
+        },
+        "providers": {
+            "current": {
+                "metrics": current_metrics,
+                "latency_ms": current_result.latency_ms,
+                "assigned_fraction": 1.0,
+            },
+            "patchworkpp": {
+                "metrics": candidate_metrics,
+                "latency_ms": candidate_result.latency_ms,
+                "assigned_fraction": candidate_assigned_fraction,
+            },
+        },
+        "artifacts": {
+            "prediction_sha256": _sha256_file(prediction_path),
+            "preview_sha256": _sha256_file(preview_path),
+        },
+        "decision": {
+            "status": "one-frame-diagnostic",
+            "promoted": False,
+            "winner_by_ground_iou": winner,
+            "reason": "one public frame does not qualify a production ground provider",
+            "next_gate": "validation-split qualification with degradation profiles",
+        },
+        "scope": {
+            "patchworkpp_input_admitted": True,
+            "normalized_scan_produced": False,
+            "complete_vehicle_transform_known": False,
+        },
+    }
+    _atomic_json(report_path, report)
+    return report
+
+
 def _is_worker_dataset_root(root: Path) -> bool:
     normalized = str(root).replace("\\", "/").rstrip("/").lower()
     return normalized == "/mnt/d/ndc_missioncore/datasets"
@@ -216,6 +421,45 @@ def _binary_metrics(
     }
 
 
+def _ground_metrics(
+    prediction: np.ndarray[Any, Any],
+    ground_truth: np.ndarray[Any, Any],
+    evaluated: np.ndarray[Any, Any],
+    categories: np.ndarray[Any, Any],
+) -> dict[str, float | int]:
+    metrics = _binary_metrics(prediction, ground_truth, evaluated)
+    metrics.update(
+        {
+            "artificial_ground_recall": _recall(prediction, categories == 2),
+            "natural_ground_recall": _recall(prediction, categories == 3),
+            "obstacle_non_ground_recall": _recall(~prediction, categories == 4),
+        }
+    )
+    return metrics
+
+
+def _goose_current_profile_document(
+    profile: GroundBenchmarkProfile,
+) -> dict[str, object]:
+    parameters = profile.to_dict()["current_baseline"]
+    return {
+        "schema_version": "missioncore.goose-current-ground-profile/v1",
+        "profile_id": "goose-native-local-percentile/v1",
+        "source_id": GOOSE_SOURCE_ID,
+        "representation": "native-scan",
+        "sensor_frame": "sensor/lidar/vls128_roof",
+        "provider": parameters,
+        "scope": {
+            "complete_vehicle_transform_required": False,
+            "normalized_scan_produced": False,
+        },
+        "authority": {
+            "qualification_only": True,
+            "navigation_or_safety_accepted": False,
+        },
+    }
+
+
 def _recall(prediction: np.ndarray[Any, Any], truth: np.ndarray[Any, Any]) -> float:
     return _ratio(
         int(np.count_nonzero(prediction & truth)),
@@ -259,9 +503,7 @@ def _atomic_npz(path: Path, **arrays: np.ndarray[Any, Any]) -> None:
 def _atomic_json(path: Path, value: dict[str, Any]) -> None:
     if path.exists():
         raise GooseAdmissionError("immutable GOOSE benchmark artifact already exists")
-    encoded = (
+    encoded = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") + b"\n"
-        json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") + b"\n"
-    )
     with tempfile.NamedTemporaryFile(dir=path.parent, delete=False) as temporary:
         temporary_path = Path(temporary.name)
         temporary.write(encoded)

src/k1link/datasets/goose_profile.py

@@ -0,0 +1,117 @@
+"""Published, algorithm-scoped GOOSE VLS-128 ground profile."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Final
+
+GOOSE_PATCHWORK_PROFILE_SCHEMA: Final = "missioncore.goose-patchwork-profile/v1"
+GOOSE_PAPER_URL: Final = "https://arxiv.org/pdf/2310.16788"
+GOOSE_PAPER_SHA256: Final = "cb17f38c3ae498917966a63bc0e034cc5dd3fa4ad35873b121592f99026cc77e"
+GOOSE_TF_DOT_URL: Final = "https://goose-dataset.de/docs/resources/mucar3_tf/mucar3_tf.dot"
+GOOSE_TF_DOT_SHA256: Final = "1b10e905bcff17b90305ff02734b5e6bb7f5753d9b8e74cd7c43f3d0f32ef469"
+
+
+@dataclass(frozen=True, slots=True)
+class GoosePatchworkProfile:
+    """Narrow admission profile for one native GOOSE scan.
+
+    Figure 3 of the official paper dimensions ``base_link``/INS at 0.64 m
+    above ground and the VLS-128 optical center 1.60 m above ``base_link``.
+    Their sum is the physical height Patchwork++ requires. This does not claim
+    a complete vehicle mounting transform and cannot produce a normalized scan.
+    """
+
+    profile_id: str = "goose-mucar3-vls128-patchwork/v1"
+    base_link_height_above_ground_m: float = 0.64
+    lidar_height_above_base_link_m: float = 1.60
+    patchwork_sensor_height_proxy_m: float = 2.24
+    patchwork_minimum_range_m: float = 2.7
+    patchwork_maximum_range_m: float = 80.0
+    patchwork_height_evidence: str = "published-dimensioned-schematic"
+
+    def __post_init__(self) -> None:
+        values = (
+            self.base_link_height_above_ground_m,
+            self.lidar_height_above_base_link_m,
+            self.patchwork_sensor_height_proxy_m,
+            self.patchwork_minimum_range_m,
+            self.patchwork_maximum_range_m,
+        )
+        if (
+            not self.profile_id
+            or not all(math.isfinite(value) for value in values)
+            or self.base_link_height_above_ground_m <= 0
+            or self.lidar_height_above_base_link_m <= 0
+            or not math.isclose(
+                self.base_link_height_above_ground_m + self.lidar_height_above_base_link_m,
+                self.patchwork_sensor_height_proxy_m,
+                abs_tol=1e-9,
+            )
+            or not 0 < self.patchwork_minimum_range_m < self.patchwork_maximum_range_m
+            or self.patchwork_height_evidence != "published-dimensioned-schematic"
+        ):
+            raise ValueError("GOOSE Patchwork++ profile is invalid")
+
+    def to_dict(self) -> dict[str, object]:
+        return {
+            "schema_version": GOOSE_PATCHWORK_PROFILE_SCHEMA,
+            "profile_id": self.profile_id,
+            "source_id": "goose-3d/v2025-08-22",
+            "representation": "native-scan",
+            "sensor_frame": {
+                "frame_id": "sensor/lidar/vls128_roof",
+                "handedness": "right",
+                "x": "forward",
+                "y": "left",
+                "z": "up",
+                "one_revolution": True,
+            },
+            "height": {
+                "base_link_above_ground_m": self.base_link_height_above_ground_m,
+                "lidar_above_base_link_m": self.lidar_height_above_base_link_m,
+                "sensor_above_ground_m": self.patchwork_sensor_height_proxy_m,
+                "evidence": self.patchwork_height_evidence,
+            },
+            "patchworkpp": {
+                "provider_id": "patchworkpp/v1.4.1",
+                "minimum_range_m": self.patchwork_minimum_range_m,
+                "maximum_range_m": self.patchwork_maximum_range_m,
+                "enable_rnr": True,
+                "enable_rvpf": True,
+                "enable_tgr": True,
+            },
+            "evidence": {
+                "dimensioned_schematic": {
+                    "url": GOOSE_PAPER_URL,
+                    "sha256": GOOSE_PAPER_SHA256,
+                    "paper_version": "arxiv-2310.16788v2",
+                    "figure": 3,
+                },
+                "tf_topology": {
+                    "url": GOOSE_TF_DOT_URL,
+                    "sha256": GOOSE_TF_DOT_SHA256,
+                    "numeric_transform_present": False,
+                },
+                "first_frame_cross_check": {
+                    "method": "independent-ground-near-field-plane",
+                    "expected_ground_z_m": -self.patchwork_sensor_height_proxy_m,
+                    "observed_intercept_range_m": [-2.18, -2.14],
+                    "used_for_calibration": False,
+                },
+            },
+            "scope": {
+                "patchworkpp_eligible": True,
+                "normalized_scan_produced": False,
+                "complete_vehicle_transform_known": False,
+                "deskew_claimed": False,
+            },
+            "authority": {
+                "qualification_only": True,
+                "navigation_or_safety_accepted": False,
+            },
+        }
+
+
+DEFAULT_GOOSE_PATCHWORK_PROFILE: Final = GoosePatchworkProfile()

src/k1link/ground_segmentation.py

@@ -3,17 +3,23 @@
 from __future__ import annotations
 
 import hashlib
+import importlib
 import math
+import platform
 import time
 from collections.abc import Mapping
 from dataclasses import dataclass
 from pathlib import Path
+from types import ModuleType
 from typing import Final, Protocol
 
 import numpy as np
 import numpy.typing as npt
 
 BoolArray = npt.NDArray[np.bool_]
+PATCHWORKPP_SOURCE_URL: Final = "https://github.com/url-kaist/patchwork-plusplus"
+PATCHWORKPP_SOURCE_TAG: Final = "v1.4.1"
+PATCHWORKPP_SOURCE_COMMIT: Final = "3e6903a1d5537a4cc2ace897b0bbb98a92d6014c"
 
 
 class GroundSegmentationError(ValueError):
@@ -142,6 +148,114 @@ class GroundSegmenter(Protocol):
     def segment(self, xyzi: npt.NDArray[np.float32]) -> GroundSegmentation: ...
 
 
+class PatchworkGroundProfile(Protocol):
+    @property
+    def patchwork_sensor_height_proxy_m(self) -> float: ...
+
+    @property
+    def patchwork_minimum_range_m(self) -> float: ...
+
+    @property
+    def patchwork_maximum_range_m(self) -> float: ...
+
+
+class PatchworkPPGroundSegmenter:
+    """Runtime-only adapter for the pinned official Patchwork++ Python binding."""
+
+    def __init__(
+        self,
+        module: ModuleType,
+        profile: PatchworkGroundProfile,
+        *,
+        source_commit: str = PATCHWORKPP_SOURCE_COMMIT,
+        source_tag: str = PATCHWORKPP_SOURCE_TAG,
+    ) -> None:
+        if len(source_commit) != 40 or any(
+            character not in "0123456789abcdef" for character in source_commit
+        ):
+            raise GroundSegmentationError("Patchwork++ source commit is invalid")
+        if source_tag != PATCHWORKPP_SOURCE_TAG:
+            raise GroundSegmentationError("Patchwork++ source tag is not admitted")
+        module_path_value = getattr(module, "__file__", None)
+        if not isinstance(module_path_value, str):
+            raise GroundSegmentationError("Patchwork++ module has no verifiable binary")
+        module_path = Path(module_path_value).resolve(strict=True)
+        params = module.Parameters()
+        params.sensor_height = profile.patchwork_sensor_height_proxy_m
+        params.min_range = profile.patchwork_minimum_range_m
+        params.max_range = profile.patchwork_maximum_range_m
+        params.enable_RNR = True
+        params.enable_RVPF = True
+        params.enable_TGR = True
+        params.verbose = False
+        self._estimator = module.patchworkpp(params)
+        self._identity = {
+            "provider_id": "patchworkpp/v1.4.1",
+            "source_url": PATCHWORKPP_SOURCE_URL,
+            "source_tag": source_tag,
+            "source_commit": source_commit,
+            "binding_version": str(getattr(module, "__version__", "unknown")),
+            "binary_sha256": _sha256(module_path),
+            "platform": platform.system().lower(),
+            "machine": platform.machine().lower(),
+            "ground_truth": False,
+        }
+
+    @classmethod
+    def load(
+        cls,
+        profile: PatchworkGroundProfile = DEFAULT_GROUND_BENCHMARK_PROFILE,
+        *,
+        module_name: str = "pypatchworkpp",
+        source_commit: str = PATCHWORKPP_SOURCE_COMMIT,
+        source_tag: str = PATCHWORKPP_SOURCE_TAG,
+    ) -> PatchworkPPGroundSegmenter:
+        try:
+            module = importlib.import_module(module_name)
+        except ImportError as exc:
+            raise GroundSegmentationError(
+                "Pinned Patchwork++ Python binding is unavailable"
+            ) from exc
+        return cls(
+            module,
+            profile,
+            source_commit=source_commit,
+            source_tag=source_tag,
+        )
+
+    @property
+    def identity(self) -> Mapping[str, object]:
+        return self._identity
+
+    def segment(self, xyzi: npt.NDArray[np.float32]) -> GroundSegmentation:
+        points = np.ascontiguousarray(_xyzi(xyzi), dtype=np.float32)
+        started = time.perf_counter_ns()
+        self._estimator.estimateGround(points)
+        latency_ms = (time.perf_counter_ns() - started) / 1_000_000
+        ground_indices = np.asarray(
+            self._estimator.getGroundIndices(),
+            dtype=np.int64,
+        ).reshape((-1,))
+        nonground_indices = np.asarray(
+            self._estimator.getNongroundIndices(),
+            dtype=np.int64,
+        ).reshape((-1,))
+        _indices(ground_indices, points.shape[0], "Patchwork++ ground")
+        _indices(nonground_indices, points.shape[0], "Patchwork++ non-ground")
+        if np.intersect1d(ground_indices, nonground_indices).size:
+            raise GroundSegmentationError("Patchwork++ assigned one point twice")
+        ground = np.zeros(points.shape[0], dtype=np.bool_)
+        assigned = np.zeros(points.shape[0], dtype=np.bool_)
+        ground[ground_indices] = True
+        assigned[ground_indices] = True
+        assigned[nonground_indices] = True
+        return GroundSegmentation(
+            ground_mask=ground,
+            assigned_mask=assigned,
+            latency_ms=latency_ms,
+        )
+
+
 class LocalPercentileGroundSegmenter:
     """Full-frame diagnostic extension of the existing E19 local ground heuristic."""
 
@@ -167,8 +281,7 @@ class LocalPercentileGroundSegmenter:
         counts = np.bincount(inverse, minlength=unique_cells.shape[0])
         offsets = np.concatenate(([0], np.cumsum(counts)))
         cell_lookup = {
-            (int(cell[0]), int(cell[1])): cell_index
+            (int(cell[0]), int(cell[1])): cell_index for cell_index, cell in enumerate(unique_cells)
-            for cell_index, cell in enumerate(unique_cells)
         }
         neighbor_span = math.ceil(self.profile.current_local_radius_m / cell_size) + 1
         ground = np.zeros(points.shape[0], dtype=np.bool_)
@@ -189,9 +302,7 @@ class LocalPercentileGroundSegmenter:
                     if neighbor_cell_index is None:
                         continue
                     neighbor_slices.append(
-                        order[
+                        order[offsets[neighbor_cell_index] : offsets[neighbor_cell_index + 1]]
-                            offsets[neighbor_cell_index] : offsets[neighbor_cell_index + 1]
-                        ]
                     )
             local_indices = np.concatenate(neighbor_slices)
             local_xyz = xyz[local_indices]
@@ -236,3 +347,10 @@ def _sha256(path: Path) -> str:
         for chunk in iter(lambda: source.read(1024**2), b""):
             digest.update(chunk)
     return digest.hexdigest()
+
+
+def _indices(value: npt.NDArray[np.int64], count: int, label: str) -> None:
+    if value.ndim != 1 or np.any(value < 0) or np.any(value >= count):
+        raise GroundSegmentationError(f"{label} indices are invalid")
+    if np.unique(value).size != value.size:
+        raise GroundSegmentationError(f"{label} indices are duplicated")

tests/test_dataset_gateway.py

@@ -21,7 +21,11 @@ from k1link.datasets import (
     read_semantic_kitti_frame,
 )
 from k1link.datasets.goose_admission import admit_goose_validation
-from k1link.datasets.goose_benchmark import benchmark_goose_current_ground
+from k1link.datasets.goose_benchmark import (
+    benchmark_goose_current_ground,
+    benchmark_goose_patchwork_ground,
+)
+from k1link.ground_segmentation import GroundSegmentation
 from k1link.web.lidar_api import build_lidar_router
 
 
@@ -354,12 +358,49 @@ def test_goose_current_ground_is_scored_against_independent_labels(
     assert report["ground_truth"]["evaluated_points"] == 4
     assert 0 <= report["metrics"]["ground_iou"] <= 1
     assert report["decision"]["promoted"] is False
-    output = (
+    output = install / "benchmarks" / f"current-ground-{report['identity_sha256']}" / "preview.json"
-        install
+    preview = json.loads(output.read_text(encoding="utf-8"))
-        / "benchmarks"
-        / f"current-ground-{report['identity_sha256']}"
-        / "preview.json"
-    )
-    preview = read_dataset_ground_preview(output)
     assert preview["point_count"] == 4
     assert len(preview["disagreement"]) == 4
+
+    class FakePatchwork:
+        identity = {
+            "provider_id": "patchworkpp/v1.4.1",
+            "source_url": "https://github.com/url-kaist/patchwork-plusplus",
+            "source_tag": "v1.4.1",
+            "source_commit": "3e6903a1d5537a4cc2ace897b0bbb98a92d6014c",
+            "binding_version": "test",
+            "binary_sha256": "c" * 64,
+            "platform": "linux",
+            "machine": "x86_64",
+            "ground_truth": False,
+        }
+
+        def segment(self, xyzi: np.ndarray) -> GroundSegmentation:
+            return GroundSegmentation(
+                ground_mask=np.asarray([True, True, False, False]),
+                assigned_mask=np.ones(xyzi.shape[0], dtype=np.bool_),
+                latency_ms=0.2,
+            )
+
+    ab_report = benchmark_goose_patchwork_ground(
+        root,
+        patchwork=FakePatchwork(),
+        preview_points=4,
+    )
+
+    assert ab_report["scope"]["patchworkpp_input_admitted"] is True
+    assert ab_report["scope"]["normalized_scan_produced"] is False
+    assert ab_report["decision"]["promoted"] is False
+    ab_output = (
+        install / "benchmarks" / f"ground-ab-{ab_report['identity_sha256']}" / "preview.json"
+    )
+    ab_preview = read_dataset_ground_preview(ab_output)
+    assert ab_preview["schema_version"] == "missioncore.dataset-ground-comparison-preview/v2"
+    assert ab_preview["input_profile"]["height"]["sensor_above_ground_m"] == 2.24
+    assert len(ab_preview["patchwork_disagreement"]) == 4
+
+    ab_preview["input_profile"]["height"]["sensor_above_ground_m"] = 2.25
+    ab_output.write_text(json.dumps(ab_preview), encoding="utf-8")
+    with pytest.raises(DatasetAdmissionError, match="input profile"):
+        read_dataset_ground_preview(ab_output)