There are 2 repositories under s3dis topic.
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
[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"
[ECCV2022] FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection
[CVPR2024] OneFormer3D: One Transformer for Unified Point Cloud Segmentation
[ICIP2023] TR3D: Towards Real-Time Indoor 3D Object Detection
[WACV'24] TD3D: Top-Down Beats Bottom-Up in 3D Instance Segmentation
PVT: Point-Voxel Transformer for 3D Deep Learning
[ICCV-23] Official implementation of SeedAL for seeding active learning for 3D 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, ...)
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis
Forked from HuguesTHOMAS KPConv_Pytorch for a course project
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.