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name: point-cnn description: "Use the legacy PointCNN TensorFlow 1.x repository to build X-Conv models, prepare point-cloud datasets, train classification or segmentation workflows, and validate evaluation artifacts." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PointCNN

Use this skill for the public yangyanli/PointCNN codebase: legacy TensorFlow 1.x point-cloud classification and segmentation built around X-Conv/X-DeConv. This is a versioned operating guide, not a copy of the source checkout. Read provenance before deciding whether a different checkout is still covered.

First route

• Model internals, X-Conv/X-DeConv, pointfly, or custom operators: read core-xconv-and-operators.
• ModelNet40, ScanNet object, TU-Berlin, Quick Draw, MNIST, or CIFAR-10 classification: read classification-workflows.
• ShapeNet Parts, S3DIS, ScanNet, or Semantic3D segmentation: read segmentation-workflows.
• Dataset download/conversion boundaries, HDF5/file lists, labels, or PLY contracts: read data-preparation.
• Prediction files, confidence merges, IoU/accuracy, or TensorBoard/checkpoint artifacts: read evaluation-and-artifacts.

For a task that crosses routes, prepare and validate data first, select a model/setting second, run a bounded workflow third, and inspect/merge artifacts last. Keep the owning sub-skill in control of its detailed contract.

Compatibility gate

This repository uses TensorFlow 1.x graph-mode APIs (tf.contrib, tf.layers, placeholders, sessions, and tf.py_func). Start with the bundled read-only probe:

python scripts/check_environment.py --help
python scripts/check_environment.py

Use a deliberately isolated legacy environment and the public dependencies listed in the repository's requirements, adapting versions to the target platform. Do not assume that a current TensorFlow release or eager execution will work. Classification requires a functioning TensorFlow 1.x graph stack.

Every supplied segmentation setting uses sampling = 'fps'. FPS, GatherPoint, and related operators are registered only for a CUDA GPU and must be built against a compatible TensorFlow framework ABI, CUDA toolkit, C++ toolchain, driver, and visible GPU. A CPU import, CPU graph build, or successful HDF5 validation is not segmentation verification. Read the FPS diagnostics and keep the required backend result visible; do not silently replace it with a CPU fallback.

Safe operating boundaries

Use explicit input and output paths. Historical shell launchers may background jobs or assume repository-relative directories; adapt them to foreground commands and disposable output roots. Do not make dataset downloads, archive extraction, full conversions, Semantic3D acquisition/decompression, long training, or benchmark runs part of a smoke check. Validate a tiny fixture or CLI help first, and record checkpoint/setting/data provenance before treating any metric as meaningful.

Cross-cutting symptoms and recovery order are in troubleshooting. The generated skill is not imported or synchronized to another agent by this creation run.