Open-source alternatives to Variational Autodecoder

Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han, "FPConv: Learning Local Flattening for Point Convolution", CVPR 2020 [paper](https://arxiv.org/abs/2002.10701)

Authors: Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone

We consider neural networks with rational activation functions. The choice of the nonlinear activation function in deep learning architectures is crucial and heavily impacts the performance of a neural network. We establish optimal bounds in terms of network complexity and prove that rational…

NVAE is a deep hierarchical variational autoencoder that enables training SOTA likelihood-based generative models on several image datasets.

Coder for "On the Continuity of Rotation Representations"

Created by Pedro Hermosilla , Tobias Ritschel , Timo Ropinski .

MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used for tasks such as 3D shape classification or segmentation. This framework includes convolution, pooling and unpooling layers which are applied directly on the mesh edges.

Update: pure PyTorch implementation of the BPS encoding is now available, thanks to Omid Taheri.

Platform for designing and evaluating Graph Neural Networks (GNN)

This repository is the official PyTorch implementation of "Implicit Graph Neural Networks".

Created by Jonas Schult, Francis Engelmann, Theodora Kontogianni and Bastian Leibe from RWTH Aachen University.

Vincent Sitzmann\, Julien N. P. Martel\, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein Stanford University, \*denotes equal contribution

This repository is the official PyTorch implementation of IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration, where we study adversarial defense in 3D point cloud and propose an deep implicit function based defense method that achieves state-of-the-art (SOTA)…

This is the project webpage of our CVPR 2020 work. RPM-Net is a deep-learning approach designed for performing rigid partial-partial point cloud registration for objects in a iterative fashion. Our paper can be found on Arxiv (supplementary).

This is the official implementation for paper "Patch-base progressive 3D Point Set Upsampling".

Relation-Shape Convolutional Neural Network for Point Cloud Analysis This repository contains the author's implementation in Pytorch for the paper:

Implementation for paper "PointMixup: Augmentation for Point Cloud". Accepted to ECCV 2020 as spotlight presentation

By Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong