mit-han-lab/bevfusion

Health v2 · maintenance only

Flags: no_releases archived

Adoption not part of the score

3230 stars · 614 forks observed · 2026-08-28

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

BEVFusion is a PyTorch-based multi-task multi-sensor fusion framework that unifies camera and LiDAR features in a shared bird's-eye view representation for 3D perception. It achieves state-of-the-art results on nuScenes, Waymo, and Argoverse 3D object detection and BEV map segmentation benchmarks.

Use cases

• fuse lidar and camera data for 3d object detection
• run bird's-eye view perception for autonomous driving
• benchmark multi-sensor fusion on nuScenes or Waymo
• train a 3d semantic segmentation model with camera and lidar
• deploy sensor fusion models to embedded hardware with TensorRT
• research multi-modal 3d perception architectures

When to choose

• you need top-accuracy camera-LiDAR fusion for 3D detection or BEV segmentation
• you want a research-proven framework with leaderboard-winning results on nuScenes, Waymo, and Argoverse
• you need a task-agnostic BEV backbone supporting multiple 3D perception heads
• you plan to deploy on NVIDIA Jetson via the official TensorRT/CUDA solutions

When to avoid

• you only have camera data with no LiDAR
• you need a lightweight production pipeline rather than a research codebase
• you lack GPU resources, since training requires significant compute
• you need active feature development - the repo is primarily a paper artifact with releases focused on maintenance

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing autonomous-vehicles computer-vision deep-learning robotics python sensor-fusion lidar camera bird-eye-view 3d-object-detection semantic-segmentation autonomous-driving pytorch nuscenes research-code gpu linux

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markdown · JSON · MCP: product_card(name="mit-han-lab/bevfusion")