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name: registration-workflows description: "Use Torch Points3D registration datasets, configs, pair/fragment contracts, descriptor evaluation notes, FPS sampling, and backend caveats." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Torch Points3D Registration Workflows

Use this sub-skill when the user asks about Torch Points3D registration tasks: 3DMatch fragments/patches, KITTI, ETH/TUM/KAIST/Planetary test sets, pair data, fragment descriptor files, FPS sampling, registration model configs, or registration-specific sparse/Open3D/backend issues.

Read First

• Read registration workflows for config families, train/test command shape, data contracts, and model choices.
• Read registration evaluation for descriptor file expectations, matching/evaluation concepts, and why repo demos are not safe default scripts.
• Read registration troubleshooting for missing pair fields, feature files, ground-truth transforms, sparse backends, Open3D, and CUDA-only assumptions.
• Run fps_registration_smoke.py for a tiny CPU check of Torch Points3D's registration FPS utility.

Main Workflows

Choose a registration config

Common data config families include:

• registration/fragment3dmatch, fragment3dmatch_dense, fragment3dmatch_partial, fragment3dmatch_sparse, and sparse single-scale variants.
• registration/patch3dmatch for patch-level 3DMatch.
• registration/fragmentkitti_sparse for KITTI fragment registration.
• registration/modelnet_sparse_ss for siamese ModelNet.
• Test/evaluation configs such as test3dmatch, testeth, testtum, testkaist, and testplanetary.

Common model config groups include registration/kpconv, registration/pointnet2, registration/pointnet2_patch, registration/minkowski, registration/spconv3d, and registration/ms_svconv_base.

Validate a pair/FPS utility path

python sub-skills/registration-workflows/scripts/fps_registration_smoke.py --json

This checks torch_points3d.datasets.registration.utils.fps_sampling using a small in-memory tensor fixture. It does not prove full 3DMatch/KITTI evaluation, but it is a safe first signal that the registration utility import path and CPU PyG/PyTorch stack are usable.

Plan descriptor evaluation

Registration evaluation scripts in the source repository assume real fragment features, ground-truth logs, optional Open3D visualization, and sometimes Minkowski/CUDA dependencies. Use registration evaluation to map required inputs before running a user's local evaluation script.

Boundary Rules

• For generic model constructor signatures and sparse backend selection, use model-apis.
• For registration data layouts that reduce to generic dataset/transform class lookup, use datasets-transforms.
• For Hydra train.py/eval.py selector syntax and checkpoint/logging behavior, use training-evaluation.

Safety Checklist

• Do not run registration demos or descriptor evaluation until the user provides required fragment/feature/ground-truth files and accepts output writes or visualization.
• Treat MinkowskiEngine, torchsparse, and CUDA as optional but required for specific sparse registration models.
• Distinguish pair/patch/fragment data contracts; a config for one family will not necessarily work with another.
• Use CPU utility smokes and config composition before heavy registration evaluation.