NVlabs/VoxFormer

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1208 stars · 102 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of VoxFormer, a CVPR 2023 highlight paper presenting a sparse voxel transformer for camera-based 3D semantic scene completion. It predicts complete 3D volumetric semantics from 2D images via a two-stage sparse-to-dense design, achieving state-of-the-art results on SemanticKITTI.

Use cases

• predict 3d semantic occupancy maps from camera images
• run semantic scene completion on semantickitti
• research camera-based 3d perception for autonomous driving
• compare 3d scene completion baselines
• reproduce cvpr 2023 voxel transformer results
• generate occupancy grid maps from monocular or stereo cameras

When to choose

• you need a strong baseline for camera-only 3D semantic scene completion
• you are researching occupancy prediction or voxel transformers for autonomous driving
• you want to benchmark on SemanticKITTI or SSCBench

When to avoid

• you need LiDAR-based scene completion rather than camera-only
• you need a production-ready perception stack rather than research code
• you lack a GPU or PyTorch training environment

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision autonomous-vehicles artificial-intelligence deep-learning python semantic-scene-completion 3d-occupancy-prediction voxel-transformer pytorch semantickitti camera-based-perception cvpr-2023 research-code linux gpu

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markdown · JSON · MCP: product_card(name="NVlabs/VoxFormer")