PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.

Pytorch framework for doing deep learning on point clouds.

🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning

[CVPR2024] OneFormer3D: One Transformer for Unified Point Cloud Segmentation

[CVPR 2022 Oral] SoftGroup for Instance Segmentation on 3D Point Clouds

[CVPR 2022 Oral] Official implementation for "Surface Representation for Point Clouds"

[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"

Grid-GCN for Fast and Scalable Point Cloud Learning

Pytorch implementation of 'Graph Attention Convolution for Point Cloud Segmentation'

[AAAI2025] UniDet3D: Multi-dataset Indoor 3D Object Detection

[CVPR 2020] FPConv: Learning Local Flattening for Point Convolution

PVT: Point-Voxel Transformer for 3D Deep Learning

[ICCV-23] Official implementation of SeedAL for seeding active learning for 3D semantic segmentation

Deep 3D Semantic Segmentation of common indoor objects on 3D point clouds .ply datas using RandLANet model with RANSAC based planar estimation post-processing techniques for prediction of vectorized floor plan of indoor scenes.

[CVPR 2021] CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation

PyTorch implementation to train MortonNet and use it to compute point features. MortonNet is trained in a self-supervised fashion, and the features can be used for general tasks like part or semantic segmentation of point clouds.

三维点云数据集下载sh脚本（目标检测，语义分割, ...）

A versatile framework for 3D machine learning built on Pytorch Lightning and Hydra [looking for contributors!]

[WACV'24] TD3D: Top-Down Beats Bottom-Up in 3D Instance Segmentation