# maudzung/SFA3D

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

Repository: https://github.com/maudzung/SFA3D
Canonical: https://ross.abutalabs.com/products/sfa3d
Homepage: https://github.com/maudzung/Super-Fast-Accurate-3D-Object-Detection
Language: Python
License: MIT
License Family: permissive
Topics: 3d-object-detection, lidar-point-cloud, bevmap, center, rtm3d, real-time, fast-detection, kitti-dataset, ros
Last push: 2023-09-17T01:57:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2200, "days_push": 1082, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1128, forks 280 (observed 2026-08-28T04:03:41.967696+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, deep-learning
- domain: autonomous-vehicles, computer-vision, deep-learning, robotics
- platform: python
- tags: lidar, 3d-object-detection, point-clouds, pytorch, kitti, ros, anchor-free, bev, gpu, linux

## Member repositories
- maudzung/SFA3D (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:41.967696+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:38:33.800321+00:00, confidence not recorded.
  - readme: https://github.com/maudzung/SFA3D (fetched 2026-08-28T04:03:41.967696+00:00, sha 6b0260c7dc56)
  - homepage: https://github.com/maudzung/Super-Fast-Accurate-3D-Object-Detection (fetched 2026-08-29T12:43:10.745029+00:00, sha 95e08ea0e82a)
- Data as of 2026-08-30T08:39:29.467469+00:00.