A list of papers and datasets about point cloud analysis (processing)

[CVPR'23] OpenScene: 3D Scene Understanding with Open Vocabularies

Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)

[ROS package] Lightweight and Accurate Point Cloud Clustering

A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"

GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

The official repository of Achelous and Achelous++

Fast and memory efficient semantic segmentation of 3D point clouds. Runs on Windows, Mac and Linux.

Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."

[IROS23] InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data

Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection

Minimum code needed to run Autoware multi-object tracking

Semantic Segmentation of Images and Point Clouds for Traversability Estimation

Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images, Applied Sciences 2021

Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation (MICCAI 2021)

Deep Learning for Computer Vision 深度學習於電腦視覺 by Frank Wang 王鈺強

ICCV 2021 papers and code focus on point cloud analysis