A list of papers and datasets about point cloud analysis (processing)
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Updated
May 19, 2023
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.
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."
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 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
Improved pytorch implementation of RandLA (https://arxiv.org/abs/1911.11236) with easier transferability and reproductibility
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