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

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

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

Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation. In ICCV2019.

Implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images" (Xie et al., ICCV 2019)

Torch Implementation of NIPS'16 paper: Perspective Transformer Nets

Source code for: Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds), accepted at ACCV 2018

(latest updates and bug fixed) DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

code for "Neural Cages for Detail-Preserving 3D Deformations"

Official implementation of GraphX-Convolution

Graph-convolutional GAN for point cloud generation. Code from ICLR 2019 paper Learning Localized Generative Models for 3D Point Clouds via Graph Convolution

Supplementary code for SHREC 2017 RGB-D Object-to-CAD Retrieval track

Given an image and a target viewpoint, generate synthetic image in the target viewpoint

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.

Learning Graph-Convolutional Representations for Point Cloud Denoising (ECCV 2020)

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