| # S3DIS Superpoints - Current Project Contract
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| ## Canonical Definition
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| - Algorithm: voxelize point coordinates, append binned normals when `normal.npy` exists, then run `np.unique(..., return_inverse=True)` on the tokenized rows to obtain per-point superpoint ids (`int32`).
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| - Default hyperparameters: `voxel_size = 0.12`, `normal_bins = 8`.
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| - Canonical generator: `PAMI2026/scripts/generate_s3dis_superpoints.py`, consistent with `pointcept_framework/scripts/visualize_s3dis_superpoints.py`.
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| - Authoritative training tree: `Haozhe2:/map-vepfs/haozhe/PAMI_superpoint/pointcept_framework`. The poplab mirror at `/mnt/data/AODUOLI/_work_biptv3/pointcept_framework` should stay aligned with it. |
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| ## Relation To Historical Data
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| - Historical `superpoint.npy` files used by earlier Haozhe2 / local runs may not be byte-identical to the canonical `0.12 / 8` generator.
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| - If paper claims and experiments must be fully self-consistent, regenerate the whole dataset with `--write`, sync the refreshed `superpoint.npy`, and retrain from those new files.
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| - If retraining is deferred, keep using the current on-disk `superpoint.npy` for both training and visualization, and do not mix regenerated visualizations with old training checkpoints without calling it out explicitly.
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| ## Canonical Training Entry
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| - Submit from poplab through `/mnt/data/AODUOLI/PAMI2026/run.py`. |
| - Default follow-up config: `configs/s3dis/semseg-pt-v3m1-0-rpe_sp_after_pool_reboot.py`. |
| - 4-GPU reboot override preset: `configs/s3dis/reboot_override.py`. |
| - Resume-style overrides should go through `MLP_OPTIONS`, for example: `MLP_OPTIONS=\"resume=True weight=exp/.../model/model_last.pth\"`. |
| - Do not copy the `PAMI2026` tree onto Haozhe2. Keep Haozhe2 compute-only under `/map-vepfs/haozhe/PAMI_superpoint/...`. |
| - If you need a Haozhe-side launcher file, render it from `run.py`; the generated file contains only Haozhe paths. |
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| ## Commands
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| ```bash
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| cd /mnt/data/AODUOLI/PAMI2026
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| # Preview only
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| python3 run.py
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| # Preview the current 4-GPU superpoint reboot launch |
| MLP_NUM_GPUS=4 \ |
| MLP_CUDA_VISIBLE_DEVICES=0,1,2,3 \ |
| MLP_CONFIG=configs/s3dis/reboot_override.py \ |
| python3 run.py |
| |
| # Render a Haozhe-local launcher without exposing poplab paths |
| MLP_RENDER_HAOZHE=1 \ |
| MLP_CONFIG=configs/s3dis/reboot_override.py \ |
| MLP_HAOZHE_LAUNCHER_OUT=outputs/run_training_haozhe.py \ |
| python3 run.py |
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|
| # Switch to another config / save_path
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| MLP_CONFIG=configs/nuscenes/semseg-pt-v3m1-0-base_nusc.py \
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| MLP_SAVE_PATH=exp/nuscenes/semseg-pt-v3m1-0-base_nusc_occfix \
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| python3 run.py
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|
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| # Real submit with resume overrides
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| export VOLC_AK=... VOLC_SK=...
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| MLP_SUBMIT=1 \
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| MLP_NUM_GPUS=4 \
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| MLP_CUDA_VISIBLE_DEVICES=0,1,2,3 \
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| MLP_OPTIONS="resume=True weight=exp/s3dis/.../model/model_last.pth" \
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| python3 run.py
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| # Inspect or rewrite superpoints
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| python scripts/generate_s3dis_superpoints.py
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| python scripts/generate_s3dis_superpoints.py --write
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| python scripts/generate_s3dis_superpoints.py --room Area_1/office_1 --write
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| # Safe candidate generation for visualization-only comparison
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| python scripts/generate_s3dis_superpoints.py --room Area_1/office_1 --write \
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| --output_root outputs/superpoint_candidates/canonical_v012_n8
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| ```
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| ## Visualization
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| - Mitsuba single-file entry: `superpoint_visualize_s3dis.py` (reads on-disk `superpoint.npy`, same as Haozhe2 when data is synced).
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| - **Batch (recommended):** `run_haozhe2_match_superpoint_vis.sh` — env toggles `DO_MITSUBA`, `DO_MITSUBA_PERCLASS`, `DO_BLENDER`; optional `FILM`, `SPP`, `MAXP`, `ROOMS`, `DATA_ROOT`, `PERCLASS_ROOM`.
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| - Example: full Mitsuba for five rooms + Blender + per-class HQ for `office_1`:
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| ```bash
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| cd /mnt/data/AODUOLI/PAMI2026
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| # 默认：Mitsuba 五场景整景 +（可按需再开 per-class / Blender）
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| bash run_haozhe2_match_superpoint_vis.sh
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| # 仅按类 HQ（office_1），可调 SPP 加速
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| DO_MITSUBA=0 DO_MITSUBA_PERCLASS=1 DO_BLENDER=0 SPP=128 bash run_haozhe2_match_superpoint_vis.sh
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| # Compare a candidate label file without touching room/superpoint.npy
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| LABEL_NPY=/mnt/data/AODUOLI/PAMI2026/outputs/superpoint_candidates/canonical_v012_n8/Area_1/office_1/superpoint.npy \
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| LABEL_TAG=canonical_v012_n8 \
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| ROOMS=office_1 DO_MITSUBA=1 DO_BLENDER=1 DO_MITSUBA_PERCLASS=0 \
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| bash run_haozhe2_match_superpoint_vis.sh
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| ```
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| - Long Mitsuba runs log to `logs/per_class_hq_latest.log` when using `tee` manually.
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| - This is unrelated to the 2D image-network model named SuperPoint.
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