A list of papers and datasets about point cloud analysis (processing)
-
Updated
May 19, 2023
A list of papers and datasets about point cloud analysis (processing)
Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022)
[ROS package] Lightweight and Accurate Point Cloud Clustering
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"
[CVPR'23] OpenScene: 3D Scene Understanding with Open Vocabularies
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.
A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
Minimum code needed to run Autoware multi-object tracking
[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"
Fast and memory efficient semantic segmentation of 3D point clouds. Runs on Windows, Mac and Linux.
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
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."
Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images, Applied Sciences 2021
Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar
Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation (MICCAI 2021)
Semantic Segmentation of Images and Point Clouds for Traversability Estimation
Trying to compute the completeness of a 3D map and compare it to another 3D map in a pointcloud format
[IROS23] InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
In this project we detect, segment and track the obstacles of an ego car and its custom implementation of KDTree, obstacle detection, segmentation, clustering and tracking algorithm in C++ and compare it to the inbuilt algorithm functions of PCL library on a LiDAR's point cloud data.
Add a description, image, and links to the point-cloud-segmentation topic page so that developers can more easily learn about it.
To associate your repository with the point-cloud-segmentation topic, visit your repo's landing page and select "manage topics."