weiyithu/SurroundOcc

Health v2 · maintenance only

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Adoption not part of the score

1081 stars · 136 forks observed · 2026-08-28

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

SurroundOcc is the official PyTorch implementation of an ICCV 2023 paper predicting dense 3D volumetric occupancy from multi-camera images for autonomous driving. It also includes a pipeline that generates dense occupancy ground truth from sparse LiDAR points using Poisson Reconstruction without extra human annotation.

Use cases

• predict 3d occupancy from multi-camera images
• generate dense occupancy ground truth from sparse lidar
• 3d semantic occupancy prediction for autonomous driving
• train a multi-camera 3d scene reconstruction model
• run occupancy prediction on private driving data
• evaluate pretrained 3d occupancy models on nuScenes

When to choose

• you need state-of-the-art multi-camera 3D occupancy prediction for driving research
• you want to create dense occupancy labels from existing 3D detection and segmentation annotations
• you are benchmarking occupancy prediction on nuScenes or similar datasets

When to avoid

• you need a production-ready perception stack rather than research code
• your project requires real-time occupancy prediction on embedded hardware out of the box
• you work outside autonomous driving or lack GPU resources for training

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing autonomous-vehicles computer-vision deep-learning artificial-intelligence python 3d-occupancy-prediction multi-camera 3d-semantic-segmentation autonomous-driving iccv-2023 lidar nuscenes research-code linux gpu

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