Super-Fast-Accurate-3D-Object-Detection
A PyTorch implementation for super fast and accurate 3D object detection using LiDAR point clouds, featuring an anchor-free approach.
What is Super-Fast-Accurate-3D-Object-Detection?
SFA3D is a PyTorch implementation for 3D object detection using LiDAR point clouds, specifically designed for autonomous driving applications. It detects objects like cars and pedestrians in 3D space with high speed and accuracy, leveraging an anchor-free approach to simplify the detection pipeline. The project is built to work with datasets like KITTI and supports real-time inference on consumer GPUs.
Researchers and engineers working on autonomous vehicles, robotics, or computer vision who need efficient 3D object detection from LiDAR data. It's also suitable for educational purposes, such as Udacity's Self-Driving Car Engineer Nanodegree.
Developers choose SFA3D for its balance of speed and accuracy without the complexity of anchor-based methods or non-max suppression. Its PyTorch implementation, pre-trained models, and ROS integration make it practical for real-world deployment and experimentation in autonomous systems.
Overview
Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)
Use Cases
Best For
- Real-time 3D object detection in autonomous driving scenarios
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