---

name: pointnet2 description: "Route PointNet2 TensorFlow 1 workflows across classification, part segmentation, ScanNet, and custom-op/API guidance." disable-model-invocation: true metadata: disco-role: operating repo: pointnet2 source-repo: charlesq34/pointnet2 license: NOASSERTION

pointnet2

Use this root skill for tasks about the charlesq34/pointnet2 TensorFlow 1.x repository: ModelNet40 classification, ShapeNetPart part segmentation, ScanNet semantic scene parsing, PointNet++ custom operators, shared TensorFlow model APIs, and point-cloud utilities.

Do not use it as a generic PointNet/PointNet++ paper summary or for unrelated PyTorch reimplementations.

Quick readiness

Work against an explicit PointNet2 checkout. The generated skill is self-contained, but the upstream project is a legacy script-style repository, not an installable Python package.

Minimum legacy stack:

• Python 2.7 plus TensorFlow 1.x. The construction smoke checks used TensorFlow 1.15 CPU; the upstream README says the original code was tested with TensorFlow 1.2 GPU, Python 2.7, Ubuntu 14.04, and CUDA-8-era custom-op compile scripts.
• numpy, h5py, scipy, scikit-learn, matplotlib, Pillow, and plyfile for the data and utility surfaces.
• Full PointNet++ train/eval/model execution additionally requires compiled TensorFlow custom ops and a matching CUDA/nvcc/TensorFlow ABI. TensorFlow import success alone is not custom-op readiness.

Minimal smoke from this skill directory:

python scripts/check_pointnet2_env.py --repo-root /path/to/pointnet2 --require tf1
python sub-skills/model-apis-and-custom-ops/scripts/inspect_custom_ops.py --repo-root /path/to/pointnet2 --require tensorflow

If a task is data-only, run the relevant bundled validator before importing original repo loaders; the ModelNet HDF5 loader can download data as a top-level side effect when its expected folder is missing.

Route by user intent

User intent	Route	First action
ModelNet40 classification training, multi-GPU training, checkpoint evaluation, voting, ModelNet layout, or classification command construction	classification-workflows	Pick HDF5 vs normal-resampled data, then use the command builder and ModelNet validator.
ShapeNetPart part segmentation, one-hot category conditioning, part labels, category splits, or legacy visualization/test path	part-segmentation-workflows	Validate ShapeNetPart layout and choose plain vs one-hot workflow before proposing a trainer command.
ScanNet semantic scene parsing, ScanNet pickle layout, label TSV columns, preprocessing, or whole-scene evaluation	scannet-semantic-scene-workflows	Validate pickles/raw scene prerequisites and label-map columns before training guidance.
TensorFlow model APIs, PointNet++ SA/MSG/FP blocks, tf_ops custom libraries, CPU pointnet_cls_basic, geometry utilities, or renderer helpers	model-apis-and-custom-ops	Check TensorFlow 1.x, distinguish custom-op presence/load readiness, then run the targeted smoke helper.
Cross-cutting Python/TensorFlow/custom-op/data/geometry failure with no workflow chosen yet	references/troubleshooting.md	Diagnose the shared failure first, then route to the owning sub-skill.

Integrated multi-route cases

• PointNet++ classification plus custom-op troubleshooting: start in classification-workflows for ModelNet40 data, model choice, checkpoint, and command construction; then use model-apis-and-custom-ops for tf_sampling, tf_grouping, tf_interpolate, CUDA/nvcc, and ABI readiness. Do not claim PointNet++ native training is runnable until the custom-op path is proven.
• ShapeNetPart plus ScanNet data preparation: route to part-segmentation-workflows and scannet-semantic-scene-workflows separately. Their validators cover different schemas; GPU/custom-op readiness is only needed for later model execution, not for layout checks.

Root references

• references/workflow-map.md: route map and cross-workflow read order.
• references/troubleshooting.md: cross-cutting legacy Python, TensorFlow 1.x, custom-op, dataset, and geometry failures.
• references/repo-provenance.md: source snapshot, dirty-state summary, and relative evidence paths.
• references/repo-routing-metadata.json: structured router metadata for repo-skill import.
• scripts/check_pointnet2_env.py: shared source-layout, dependency, TensorFlow, custom-op, and dataset readiness check.

Hard limits to preserve

• This repo mixes Python 2 syntax, TensorFlow 1.x APIs, and CUDA-era custom-op compile assumptions. Python 3 or TensorFlow 2 import success is not sufficient proof of workflow readiness.
• Downloaded datasets and trained checkpoints are external to this generated skill. The skill describes layouts and provides validators; it does not ship ModelNet40, ShapeNetPart, ScanNet, or checkpoints.
• Raw training/evaluation scripts are treated as legacy source workflows. Prefer the bundled command builders and validators before asking a user to run original scripts.