Pytorch implementation of 'Graph Attention Convolution for Point Cloud 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.

Grid-GCN for Fast and Scalable Point Cloud Learning

Forked from HuguesTHOMAS KPConv_Pytorch for a course project

Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis

CVPR 2020, "FPConv: Learning Local Flattening for Point Convolution"

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

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

PVT: Point-Voxel Transformer for 3D Deep Learning

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

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

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

Datahub to the Applied Science Paper: Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey by Lukas Rauch et al.

[ICIP2023] TR3D: Towards Real-Time Indoor 3D Object Detection

[ECCV2022] FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

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

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

三维点云数据集下载sh脚本（目标检测，语义分割, ...）

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

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