docs(perception): record PointPillars transfer verdict

docs/13_LIDAR_WORKER_PRODUCT_AND_ROADMAP.md

@@ -620,21 +620,96 @@ Exit: one immutable K1 session yields both a persistent reconstruction and a
 bounded local world state without hard-coded terrain height or scanner-side
 changes.
 
-### L3 — LiDAR-native 3D detection — deferred behind L2.6
+### L3 — LiDAR-native 3D detection — active after L2.6
 
-- [ ] Establish the public-dataset baseline first; freeze K1-specific 3D
+- [x] Freeze a fail-closed benchmark admission that distinguishes independent
-  annotations only when a measured domain gap justifies them.
+  oriented 3D box truth from point-wise semantic/instance labels.
-- [ ] Run NVIDIA PointPillars through the existing external worker/Triton seam.
+- [x] Extract and seal the contract from the exact NVIDIA ONNX instead of
-- [ ] Treat pretrained output as a baseline, not an accepted product model.
+  copying a generic KITTI example range into the product profile.
-- [ ] Measure class precision/recall, center/range/yaw error, distance-bucket
+- [x] Build the target TensorRT engine on Worker 006, install it into the
-  recall, false occupied objects and end-to-end latency.
+  existing canonical Triton repository and execute both GOOSE and KITTI schema
+  smokes without creating or restarting a second serving stack.
+- [x] Admit all KITTI 3D Object Detection 2017 archives and the pinned complete
+  `3,712 / 3,769` train/validation split on Worker 006.
+- [x] Measure the complete public cross-domain transfer probe through the
+  canonical worker seam: `3,769/3,769` frames, fixed postprocess and no
+  validation retuning.
+- [x] Reject the exact `deployable_v1.1` checkpoint as a K1 product candidate:
+  runtime is healthy, while BEV mAP40 is `2.46322%`, 3D mAP40 is
+  `0.000216745%` and false occupied rate is `99.9981%`.
+- [x] Preserve the claim boundary: the NGC model card's proprietary
+  solid-state-LiDAR result is not independently reproducible, KITTI is
+  cross-domain, and `RAVNOVES01` without independent cuboids cannot establish
+  detector accuracy.
+- [ ] Freeze a separate training/adaptation admission before downloading a
+  trainable checkpoint or starting a training runtime.
+- [ ] Train only on the frozen `3,712` KITTI train frames; keep all `3,769`
+  validation frames untouched until the candidate ONNX is sealed.
+- [ ] Rebuild and evaluate the adapted engine through the already proven
+  Worker 006/Triton/postprocess seam.
+- [ ] After a useful public box-truth result, run `RAVNOVES01` as a K1
+  deterministic-replay, schema, latency, queue/drop and visual-plausibility
+  transfer. Do not call it a K1 accuracy benchmark.
 - [ ] Compare Autoware CenterPoint only after the PointPillars harness is stable.
-- [ ] Fine-tune only if the baseline demonstrates useful transfer and the
-  annotation budget is justified.
 
 Exit: the selected detector beats the camera-derived cuboid baseline on the
 independent gate without increasing unsafe false-free or false-dynamic output.
 
+The current immutable L3 admission result is
+`l3-pointpillars-admission-857a8c6a958850f82db7a58e894e13e2a54add09379ca58fb8e47db1e6aba65c`.
+It freezes NGC `nvidia/tao/pointpillarnet:deployable_v1.1` as ONNX digest
+`2dcabddc3a365e9608a112d7bbbb7db769a6dddeeaa59aa03611a83113326da1`.
+The signed companion label file has digest
+`0adaeb5a374421b61bf83b8fa4522e11abd68461f239a4c72cf5627de913b3da`
+and names `Vehicle`, `Pedestrian`, `Cyclist`. The evaluation profile explicitly
+maps only `Vehicle → Car`; the other two class names are identity mappings.
+The decoded row contract and class-agnostic BEV NMS are pinned to NVIDIA's
+`tao_toolkit_recipes` commit
+`a540badc47812a17a94e924b537d49ad3969b5a8`: `4,096` pre-NMS candidates and
+IoU threshold `0.01`. The ONNX itself embeds score threshold `0.1` and the
+voxel range `[-51.2, -51.2, -1.4]` to `[51.2, 51.2, 4.4]`; the sealed graph
+contract has digest
+`2fd29cd054ab058c2cfec3dfba305c71e123ef3f04b457d0c64de0c8dac2e1be`.
+Postprocessing executes on Worker 006; the raw
+`[1, 393216, 9]` tensor is never a Mac/browser transport contract.
+The public metric is explicitly a cross-domain transfer probe, not native
+checkpoint accuracy. It is a frozen local 40-point AP gate without KITTI
+difficulty filtering or test-server submission: IoU is `0.7` for `Car` and
+`0.5` for `Pedestrian/Cyclist`, with distance recall buckets
+`0–20 / 20–40 / 40–70 m`.
+Worker 006 built a TensorRT 11.0 engine for its RTX 4090 with digest
+`12005d972a4632d56342a5da44442b632c1dcc5144fa3c70b162dec334532481`.
+The build completed in `9.139 s`, used approximately `192 MiB` peak builder
+GPU memory and stayed inside the existing `ndc-mission-core-triton` container.
+The model was installed into the existing repository and loaded through
+explicit model control without restarting Triton.
+
+One GOOSE native XYZI frame with `169,883` points then passed a single-query
+representation smoke at `55.2069 ms` GPU compute time. This proves only that
+the admitted sensor-frame XYZI shape can execute through the staged engine.
+GOOSE and RELLIS provide independent point-wise semantic/instance truth, not
+oriented 3D cuboids, so they cannot produce PointPillars 3D mAP, center or yaw
+accuracy. KITTI 3D Object Detection 2017 is the independent public
+cross-domain box-truth source under `CC-BY-NC-SA-3.0`, with the OpenPCDet split
+at commit
+`233f849829b6ac19afb8af8837a0246890908755`. Its admission is archive-only and
+fail-closed: three official archive sizes and digests, XYZI packing, all
+training/test calibrations, all training label rows, and the disjoint complete
+`3,712 / 3,769` train/validation partition are verified before a path-free
+state may be published. The source archives stay on Worker 006 and are not
+silently copied to an operator host.
+
+The complete immutable transfer probe is
+`l3-pointpillars-kitti-1a6b499e194a363644854dc324bd1b565c100b809f145c1324a25328e7ae0910`.
+It processed `3,769/3,769` frames sequentially. Mean inference was
+`31.1366 ms`, p95 was `45.9418 ms` and maximum was `68.8731 ms`. Of `315,589`
+post-NMS boxes, `159,263` were inside the shared model/KITTI evaluation volume;
+the remaining `156,326` were explicitly excluded rather than scored against
+an uncovered volume.
+
+The exact contract, metrics, rejection decision and reproduction notes are in
+`experiments/perception/L3_POINTPILLARS_TRANSFER_REPORT_2026-07-31.md`.
+
 ### L4 — live shadow integration
 
 - [x] Add a provider-neutral bounded LiDAR local-surface queue independent of

experiments/perception/L3_POINTPILLARS_TRANSFER_REPORT_2026-07-31.md

@@ -0,0 +1,251 @@
+# L3 PointPillars transfer report — 2026-07-31
+
+## Decision
+
+The LiDAR-native detector seam is operational on Worker 006, but the frozen
+NVIDIA deployable checkpoint is **rejected as a K1 product detector
+candidate**.
+
+The result is not a runtime failure. The exact ONNX model was built on the
+target GPU, loaded into the existing canonical Triton service and evaluated
+over all `3,769` frozen KITTI validation frames. Runtime latency is acceptable,
+but the public cross-domain accuracy is not.
+
+Immutable admission:
+
+`l3-pointpillars-admission-857a8c6a958850f82db7a58e894e13e2a54add09379ca58fb8e47db1e6aba65c`
+
+Immutable transfer probe:
+
+`l3-pointpillars-kitti-1a6b499e194a363644854dc324bd1b565c100b809f145c1324a25328e7ae0910`
+
+Worker package:
+
+`l3-pointpillars-worker-package-c219d3b35be63b03d68be318a0709af2422aa67ae12561cec6c1de6252abad12`
+
+The next gate is a separately admitted training/adaptation candidate. The
+current deployable checkpoint must not be promoted into a detector claim,
+navigation, safety or camera-replacement path.
+
+## Corrected model contract
+
+The generic KITTI example configuration is not the contract of the downloaded
+NGC checkpoint. The source of truth is the exact ONNX graph with SHA-256:
+
+`2dcabddc3a365e9608a112d7bbbb7db769a6dddeeaa59aa03611a83113326da1`.
+
+Read-only graph inspection produced the sealed evidence file
+`l3_pointpillars_onnx_contract_2026-07-31.json`, SHA-256:
+
+`2fd29cd054ab058c2cfec3dfba305c71e123ef3f04b457d0c64de0c8dac2e1be`.
+
+The embedded contract is:
+
+- input: `points [batch, 204800, 4] FP32`;
+- count: `num_points [batch] INT32`;
+- fields: LiDAR-frame `x, y, z, intensity`;
+- voxel range:
+  `[-51.2000008, -51.2000008, -1.39999998]` to
+  `[51.2000008, 51.2000008, 4.4000001]`;
+- voxel size: `[0.2, 0.2, 5.8]`;
+- maximum voxels: `10,000`;
+- maximum points per voxel: `32`;
+- embedded score threshold: `0.1`;
+- native labels: `Vehicle`, `Pedestrian`, `Cyclist`;
+- decoded output:
+  `output_boxes [batch, 393216, 9] FP32` plus
+  `num_boxes [batch] INT32`;
+- decoded row:
+  `x, y, z, length, width, height, yaw, class_id, score`.
+
+External postprocessing is pinned to NVIDIA
+`tao_toolkit_recipes@a540badc47812a17a94e924b537d49ad3969b5a8`:
+stable descending score order, `4,096` pre-NMS candidates and class-agnostic
+oriented-BEV NMS at IoU `0.01`.
+
+The NGC model card states that this checkpoint was trained and evaluated on a
+proprietary solid-state LiDAR dataset. Its native accuracy cannot be
+independently reproduced from the public model package. KITTI is therefore a
+public **cross-domain transfer probe**, not a native model benchmark.
+
+## Runtime evidence
+
+No second container or serving stack was created.
+
+- canonical service: `ndc-mission-core-triton`;
+- image:
+  `nvcr.io/nvidia/tritonserver:26.06-py3@sha256:58df7489c3f2276f9591d500a012dee03e23d35543ce3c390b4c001e6bf90794`;
+- model control: explicit;
+- strict readiness: enabled;
+- model repository mount: read-only;
+- GPU: NVIDIA GeForce RTX 4090, compute capability `8.9`;
+- TensorRT: `11.0.0`;
+- precision policy: strongly typed;
+- engine SHA-256:
+  `12005d972a4632d56342a5da44442b632c1dcc5144fa3c70b162dec334532481`;
+- engine size: `8,785,436` bytes;
+- target build time: `9.13932 s`;
+- observed peak builder allocation: approximately `192 MiB`.
+
+The model was added to the existing repository and loaded through Triton's
+explicit load endpoint. The container was not restarted.
+
+One GOOSE native XYZI frame with `169,883` points passed a representation
+smoke at `55.2069 ms` GPU compute time. One KITTI frame with `120,268` points
+also passed the live schema smoke. These smokes prove input/output
+compatibility only.
+
+## Dataset admission
+
+KITTI 3D Object Detection 2017 was admitted archive-only on Worker 006:
+
+- release identity:
+  `2c9615bedca56b492b204b614d4419db6431626e2a6867e207a95999beefcf47`;
+- labeled training frames: `7,481`;
+- official test frames: `7,518`;
+- split source: OpenPCDet commit
+  `233f849829b6ac19afb8af8837a0246890908755`;
+- train split: `3,712`, SHA-256
+  `b6417a1d9b18c8fdb085128e633d28ff321b7674a6d1b3841b8f43d865b281cb`;
+- validation split: `3,769`, SHA-256
+  `657ac4bcc1e156e5b106a4ca18e1f88e012787ea1d2b5d0adeea97fee903fa86`.
+
+The archives remain on Worker 006 and were not copied to the Mac. Admission
+verifies official archive byte lengths and digests, safe ZIP structure, XYZI
+packing, label/calibration alignment, finite positive target cuboids and the
+complete disjoint split.
+
+GOOSE and RELLIS remain valid for representation, semantic and instance
+experiments. Their point-wise labels are not silently converted into oriented
+3D-box truth.
+
+## Evaluation boundary
+
+KITTI's common detector volume and the embedded NVIDIA checkpoint volume are
+different. The measured intersection is:
+
+`[0, -39.68, -1.39999998]` to `[51.2000008, 39.68, 1]`.
+
+Predictions outside this shared volume are excluded rather than counted as
+false positives against a region the probe does not jointly cover.
+
+The metric is deliberately not the official KITTI server metric:
+
+- 40-point interpolated BEV and 3D AP;
+- no KITTI easy/moderate/hard filtering;
+- IoU `0.7` for `Car`;
+- IoU `0.5` for `Pedestrian` and `Cyclist`;
+- fixed mapping `Vehicle → Car`;
+- no validation retuning;
+- one sequential worker process.
+
+## Complete transfer result
+
+All `3,769/3,769` validation frames completed.
+
+| Metric | Result |
+| --- | ---: |
+| BEV mAP40 | `0.0246322` (`2.46322%`) |
+| 3D mAP40 | `0.00000216745` (`0.000216745%`) |
+| False occupied rate | `0.999981` (`99.9981%`) |
+| Mean inference latency | `31.1366 ms` |
+| P50 inference latency | `29.0994 ms` |
+| P95 inference latency | `45.9418 ms` |
+| Maximum inference latency | `68.8731 ms` |
+
+Per class:
+
+| Class | BEV AP40 | 3D AP40 | TP | FP | GT |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Car | `0.0734788` | `0.0000034125` | `2` | `71,888` | `12,912` |
+| Pedestrian | `0.0000400679` | `0.00000308985` | `1` | `12,612` | `2,239` |
+| Cyclist | `0.000377644` | `0` | `0` | `74,760` | `801` |
+
+Prediction accounting:
+
+- post-NMS model boxes: `315,589`;
+- boxes inside the shared evaluation volume: `159,263`;
+- boxes outside the shared volume and explicitly ignored: `156,326`.
+
+The frame-result set identity is:
+
+`30b1933d508a09025a7d3c3c460fc2d06128e4bbe96a753bec7ba8545fda3e9c`.
+
+The compact report SHA-256 is:
+
+`f53d8d03a7c1e092d87b732872b26fea16447f373cf567bfe57737cb3ccbebba`.
+
+## Interpretation
+
+The worker seam, TensorRT engine, Triton serving path, bounded postprocess and
+full-dataset runner are usable. The pretrained checkpoint is not.
+
+The result demonstrates severe domain mismatch; it does not demonstrate that
+PointPillars as an architecture is unsuitable for K1. It demonstrates that
+this exact proprietary-domain checkpoint cannot be used as the product
+detector without an admitted adaptation/training step.
+
+No threshold was tuned to improve the result. Changing the score threshold on
+the frozen validation output would be validation leakage and would not repair
+the sensor-domain mismatch.
+
+## Next gate
+
+1. Freeze a new training/adaptation admission. It must name the trainable
+   checkpoint, training container/toolchain, dataset identities, resource
+   limits and output model identity before execution.
+2. Train only on the frozen `3,712` KITTI train frames.
+3. Keep all `3,769` validation frames untouched until the candidate is sealed.
+4. Export a new ONNX, build its TensorRT engine on Worker 006 and evaluate
+   through the already proven canonical Triton seam.
+5. Require a useful public box-truth result before K1 accuracy language.
+6. Run `RAVNOVES01` as a K1 representation/stability transfer after the public
+   candidate is useful. Because that session has no camera and no independent
+   3D cuboids, it can prove deterministic replay, output stability, latency,
+   queue/drop behavior and visual plausibility, but not detector accuracy.
+
+The adaptation task is a new admission. This report does not authorize a
+training container, a second serving stack, K1 quality claims or navigation
+authority.
+
+## Claim boundary
+
+Not proved:
+
+- native accuracy of the proprietary NVIDIA checkpoint;
+- K1 detector precision, recall, mAP or range/yaw accuracy;
+- that absence of a detection means free space;
+- superiority over the camera-first object candidate;
+- navigation or safety fitness.
+
+Explicitly false:
+
+- command authority;
+- navigation authority;
+- safety acceptance;
+- camera-first replacement;
+- LAB product publication.
+
+## Reproduction
+
+Admission:
+
+```bash
+uv run python experiments/perception/run_l3_pointpillars_admission.py \
+  --profile experiments/perception/l3_pointpillars_benchmark_profile.json \
+  --dataset-inventory experiments/perception/l3_dataset_inventory_2026-07-30.json \
+  --worker-inventory experiments/perception/l3_worker_inventory_2026-07-30.json
+```
+
+The Worker package is built by:
+
+```bash
+uv run python experiments/perception/prepare_l3_pointpillars_worker_package.py \
+  --repository-root . \
+  --output-root <worker-package-output> \
+  --admission-result <accepted-admission-result>
+```
+
+The complete probe runs only from the exact package on Worker 006 with
+`PYTHONPATH=<package>/runtime`, Python `-B`, the admitted KITTI root, the
+canonical model provenance and the existing local Triton endpoint.