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name: point-cloud-workflows description: "Operate the DeepGCNs point-cloud workflows for ModelNet40 classification, S3DIS dense or sparse semantic segmentation, and PartNet part segmentation, including tensor contracts, exact configuration flags, checkpoint/evaluation boundaries, and safe synthetic verification." disable-model-invocation: true metadata: disco-role: operating license: MIT

Point-cloud workflows

Use this skill for a point-cloud task involving ModelNet40 classification, S3DIS semantic segmentation, or PartNet part segmentation. It is a reference workflow, not a training runner: do not download data, fetch checkpoints, run long training, launch distributed jobs, or open a visualization GUI from this skill.

Route first

• ModelNet40 classification: follow workflows.md and the ModelNet section.
• S3DIS semantic segmentation: choose dense or sparse using the decision rule in workflows.md, then validate the layout in data-formats.md.
• PartNet part segmentation: follow partnet.md; raw-data preparation and checkpoint/category matching are mandatory before evaluation.
• Generic GCN layers, EdgeConv/MRConv, KNN, blocks, GENConv, or reversible primitives: route to the sibling graph-layers skill. This skill only records the task-level layout and configuration needed to use those layers.
• OGB or PPI: route to the sibling ogb-workflows or ppi-workflows skill. Do not substitute S3DIS, PartNet, or ModelNet conventions for those datasets.

Safe operating sequence

The command blocks in the references are non-executable command shapes for an independently staged implementation. Replace only their neutral entrypoint and resource placeholders; never use them to open or run files from an original source checkout. The bundled smoke below is the only direct executable in this skill.

1. State the task, dataset split, category/area, point count, feature count, dense/sparse layout, backend/device, and whether a checkpoint is supplied.

2. Run the bundled smoke from any current working directory before importing a project model:

   python <skill-root>/scripts/pointcloud_model_smoke.py --help
   python <skill-root>/scripts/pointcloud_model_smoke.py --mode all
   

   The script is self-contained and uses only a tiny synthetic fixture. It does not import gcn_lib, PyG, torch_cluster, a dataset, or a checkpoint.

3. Pre-stage data and checkpoints outside the skill. Treat every documented automatic download as disabled for this operating path.

4. Use the task's exact parser flags and verify that the checkpoint's class count, block, convolution, filters, blocks, KNN settings, category/area, and layout agree with the current request.

5. Start with the smallest non-distributed evaluation or forward pass. Reduce points, k, batch size, or blocks when diagnosing memory; do not infer benchmark-quality results from the smoke.

Invariants

• Coordinates are the first three channels. S3DIS normally supplies 3-D positions plus six additional features (in_channels=9); ModelNet and the default PartNet path use positions only (in_channels=3).
• Dense point-cloud models consume B x C x N x 1; sparse models consume node features N x C plus a node-to-graph batch vector of length N.
• Classification returns one logit vector per cloud. Segmentation returns one logit vector per point; never pool away the point axis for S3DIS or PartNet.
• k is the requested neighborhood width and dilation internally asks for k * dilation candidates. It must not exceed the number of points in each cloud. Matrix KNN has quadratic point-count memory.
• --use_cpu/CPU availability selects the device, but installed PyTorch, PyG, torch_scatter, and torch_cluster binaries still need to be ABI and backend compatible. See troubleshooting.md.

Scope boundary

The references preserve source-observed commands, defaults, data fields, and failure modes without copying full training programs. They deliberately omit network downloads, external-drive checkpoint retrieval, distributed launch recipes as executable actions, and VTK execution. A result is not verified until the requested data/checkpoint and backend are separately available and an appropriate task-level test has passed.