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

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

[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++

[IROS 23] InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data

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

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)

Codes for automatic point-cloud-to-BIM conversion

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

3D Human Part Segmentation with Point Transformer

FLS point cloud registration (ISRR 2022)

A High-Efficient Research Development Toolkit for Image Segmentation Based on Pytorch.

Python scripts for converting mesh formats, mesh simplification and mesh rigid transformation

PCD annotation processing and guideline based on Semantic Segmentation Editor

Implementing a PointNet based architecture for classification and segmentation with point clouds. Q1 and Q2 focus on implementing, training and testing models. Q3 asks you to quantitatively analyze model robustness.

Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing

This is a repository mainly about IEEE data fusion contest 2019 track 4 — Cloud points classification