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)
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
[IROS23] InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
Trying to compute the completeness of a 3D map and compare it to another 3D map in a pointcloud format
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.
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