maudzung/SFA3D

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1128 stars · 280 forks observed · 2026-08-28

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

A PyTorch implementation of SFA3D, a fast and accurate anchor-free 3D object detection model for LiDAR point clouds, trained and evaluated on the KITTI dataset. It includes pre-trained models, training scripts with distributed data parallel support, and optional ROS integration.

Use cases

• detect 3d objects from lidar point clouds
• run real-time 3d object detection on kitti dataset
• train a lidar object detection model in pytorch
• integrate 3d detection into a ros pipeline
• learn how lidar-based perception works for self-driving cars

When to choose

• you need fast, real-time 3D object detection from LiDAR point clouds
• you want an anchor-free detector without non-max suppression
• you are working with the KITTI dataset or similar LiDAR data
• you need a reference implementation for learning or teaching autonomous vehicle perception

When to avoid

• you need camera-only or multi-modal (camera + LiDAR) fusion detection
• you need a production-supported, actively maintained framework
• you work with datasets other than KITTI without adaptation effort
• you lack a GPU, since training and inference are GPU-oriented

Facets

library · maturity maintenance

computer-vision machine-learning deep-learning autonomous-vehicles computer-vision deep-learning robotics python lidar 3d-object-detection point-clouds pytorch kitti ros anchor-free bev gpu linux

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markdown · JSON · MCP: product_card(name="maudzung/SFA3D")