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name: voxformer description: "Use the NVlabs VoxFormer repository for camera-only 3D semantic scene completion on SemanticKITTI: install its legacy CUDA/OpenMMLab stack, validate data, select QPN/VoxFormer configurations, and plan safe training or evaluation." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

VoxFormer

Use this repo skill when a request names VoxFormer, camera-only 3D semantic scene completion, QPN/query proposals, SemanticKITTI occupancy, .pseudo voxels, VoxFormer-S/T, or the deform3D variants. VoxFormer is an application checkout, not a self-contained pip package. Its documented pipeline is:

1. Prepare SemanticKITTI-derived images, calibration, poses, voxel inputs, labels, and (for stage 2) query proposals.
2. Run stage 1, the class-agnostic query proposal network (qpn.py).
3. Feed the stage-1 query artifacts to stage 2, VoxFormer-S (single image) or VoxFormer-T (temporal references), and evaluate semantic scene completion.

The standard model path requires a compatible NVIDIA CUDA runtime and the legacy OpenMMLab versions documented in the installation route. CPU can help with static config and layout checks, but it is not a truthful substitute for model execution or native CUDA operators. Full training, evaluation, data regeneration, and checkpoint downloads are user-authorized, expensive actions; this skill's bundled checks never launch them.

Route by request

• Install, import, CUDA, native operators, or optional depth environment: environment-and-installation.
• SemanticKITTI tree, labels, pseudo-LiDAR, voxelization, or query files: dataset-preparation.
• QPN/S/T/deform3D preset selection, architecture fields, dimensions, or registry/config errors: model-configuration.
• Train/test commands, distributed launch, checkpoints, or SSC metrics: training-and-evaluation.

For a cross-stage request, use the routes in that order: environment, data, config, then train/test. Read the linked reference before constructing a command. All bundled scripts are read-only preflight tools and must be run with --help first.

Minimal public environment contract

Start with a fresh Python 3.8 environment and compare the documented legacy family: PyTorch 1.9.1/cu111, torchvision 0.10.1, mmcv-full 1.4.0, mmdet 2.14.0, mmsegmentation 0.14.1, mmdetection3d v0.17.1 from source, and a compatible timm release. Do not install these into an unrelated working environment without approval. After installation, run the read-only checker with a user-supplied checkout placeholder:

python sub-skills/environment-and-installation/scripts/check_environment.py --help
python sub-skills/environment-and-installation/scripts/check_environment.py --repo-root <VOXFORMER_ROOT>

Treat missing CUDA/native operators as a blocked runtime layer, not as a CPU success. The environment sub-skill contains the build and import sequence.

Non-negotiable safety checks

• Do not start stage 2 until its configured query files, pseudo-voxel inputs, labels, and image/frame alignment pass the data checker. A stage-1 checkpoint is not stage-2 query data.
• Do not use a CPU import as evidence that a CUDA model or custom extension is executable. Check mmdet3d.ops, MMCV's deformable attention, and the selected extension separately.
• Standard S/T configs and the custom *_deform3D.py configs are different backend variants. The custom wrapper in the public checkout contains a deliberate extension-search-path placeholder and raises until an operator supplies a valid built-extension location; never claim the unmodified custom import is ready.
• Do not run repository shell loops, download weights/data, or overwrite work directories as part of a read-only preflight. Quote user-supplied paths and review generated commands before execution.
• Respect the repository's NVIDIA Source Code License-NC and the separate non-commercial terms for pretrained models.

Quick entry points

• Environment: sub-skills/environment-and-installation/scripts/check_environment.py
• Data: sub-skills/dataset-preparation/scripts/validate_dataset_layout.py
• Config: sub-skills/model-configuration/scripts/validate_config.py
• Train/test plan: sub-skills/training-and-evaluation/scripts/preflight_train_test.py
• Cross-cutting failures: references/troubleshooting.md

Scope and verification boundary

This skill is distilled from the public repository at the pinned source commit recorded in repo-provenance.md. It does not bundle datasets, checkpoints, teaser media, the embedded MobileStereoNet implementation, or generated platform-specific binaries. The included verification records distinguish safe import/build smokes from unrun full-scale training and SemanticKITTI evaluation; consult the nearest sub-skill reference rather than inferring a result from the paper's metrics.

Bundled references

• Troubleshooting
• Provenance
• Routing metadata