Pytorch framework for doing deep learning on point clouds.

PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

[ECCV 2022] SimpleRecon: 3D Reconstruction Without 3D Convolutions

Point-NeRF: Point-based Neural Radiance Fields

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

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

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

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

4D Spatio-Temporal Semantic Segmentation on a 3D video (a sequence of 3D scans)

[WACV2022] ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

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

LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs (CVPR 2023)

Grid-GCN for Fast and Scalable Point Cloud Learning

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

[ECCV'20] Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation

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

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

FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image

This work is based on our paper "DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes", which appeared at the IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 2020.

[ECCV 2024] 3D Small Object Detection with Dynamic Spatial Pruning