Popular GitHub Repositories for Lidar Point Cloud

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

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

A list of references on lidar point cloud processing for autonomous driving

Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation

The official implementation for "Spherical Transformer for LiDAR-based 3D Recognition" (CVPR 2023).

Implementation for CenterFormer: Center-based Transformer for 3D Object Detection (ECCV 2022)

Lidar Obstacle Detection

Implementation of SqueezeSegV2, Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Variants of Vision Transformer and its downstream tasks

Implementation of IROS20 paper - "Semantic Graph Based Place Recognition for 3D Point Clouds"

Exploring Simple 3D Multi-Object Tracking for Autonomous Driving (ICCV 2021)

Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)

This repository contains the PyTorch implementation of the ECCV'2022 paper, ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection.

Source code for the article "Robust LiDAR Feature Localization for Autonomous Vehicles Using Geometric Fingerprinting on Open Datasets"

This repo detect objects automatically for LiDAR data

2D road segmentation using lidar data during training

Complex YOLO ROS is a 3D object detection system interfaced with ROS, enabling real-time robotics applications. It utilizes Lidar data and deep learning techniques for accurate detection and localization of objects in complex environments

[IEEE RA-L 2024] This repository contains the implementation code for the paper "P-GAT : Pose-Graph Attentional Network for Lidar Place Recognition".

Latest main K9 robot repository with 3D vision, local STT/TTS with GPT-3 and 360 LIDAR.

Tensorflow 2.9 Pipeline for Semantic Point Cloud Segmentation with SqueezeSeqV2, Darknet21 and Darknet53.