Pytorch framework for doing deep learning on point clouds.
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Updated
Aug 5, 2024 - Python
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
[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)
[CVPR2024] OneFormer3D: One Transformer for Unified Point Cloud Segmentation
[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
[ECCV'20] Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
CVPR 2020, "FPConv: Learning Local Flattening for Point Convolution"
[WACV'24] TD3D: Top-Down Beats Bottom-Up in 3D Instance Segmentation
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.
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
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