There are 33 repositories under lidar-point-cloud topic.
The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"
KITTI Object Visualization (Birdview, Volumetric LiDar point cloud )
Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
A list of references on lidar point cloud processing for autonomous driving
Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation
Official page of ERASOR (Egocentric Ratio of pSeudo Occupancy-based Dynamic Object Removal), which is accepted @ RA-L'21 with ICRA'21
A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)
A probabilistic voxelmap-based LiDAR-Inertial Odometry.
Implementation for CenterFormer: Center-based Transformer for 3D Object Detection (ECCV 2022)
The official implementation for "Spherical Transformer for LiDAR-based 3D Recognition" (CVPR 2023).
Implementation of SqueezeSegV2, Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
Lidar Obstacle Detection
This repository contains all the work that I regularly did and studied from Medium blogs, several research papers, and other Repos (related/unrelated to the research papers).
The code implemented in ROS projects a point cloud obtained by a Velodyne VLP16 3D-Lidar sensor on an image from an RGB camera.
Variants of Vision Transformer and its downstream tasks
An efficient, extensible occupancy map supporting probabilistic occupancy, normal distribution transforms in CPU and GPU.
Ground segmentation benchmark in SemanticKITTI dataset
LiDAR snowfall simulation
Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments
Create Dense Depth Map Image for Known Poisitioned Camera from Lidar Point Cloud
LiDAR fog simulation
Repository for automatic classification and labeling of Urban PointClouds using data fusion and region growing techniques.