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)

Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"

[ROS package] Lightweight and Accurate Point Cloud Clustering

[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"

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

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

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

Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar

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

[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 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)

Semantic Segmentation of Images and Point Clouds for Traversability Estimation

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