[CVPR 2021 Oral] Variational Relational Point Completion Network

Overview

VRCNet: Variational Relational Point Completion Network

This repository contains the PyTorch implementation of the paper:

Variational Relational Point Completion Network, CVPR 2021 (Oral)

[arxiv|video|webpage]

In CVPR 2021

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point selfattention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute a multi-view partial point cloud dataset (MVP dataset) containing over 100,000 high-quality scans, which renders partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-theart methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans.

VRCNet architecture overview:

Our proposed point cloud learning modules:

Point Cloud Completion Benchmark

Moreover, this repository introduces an integrated Point Cloud Completion Benchmark implemented in Python 3.5, PyTorch 1.2 and CUDA 10.0. Supported algorithms: PCN, Topnet, MSN, Cascade, ECG and our VRCNet.

Installation

  1. Install dependencies:
  • h5py 2.10.0
  • matplotlib 3.0.3
  • munch 2.5.0
  • open3d 0.9.0
  • PyTorch 1.2.0
  • PyYAML 5.3.1
  1. Download corresponding dataset (e.g. MVP dataset)

  2. Compile PyTorch 3rd-party modules (ChamferDistancePytorch, emd, expansion_penalty, MDS, Pointnet2.PyTorch)

MVP Dataset

Please download our MVP Dataset to the folder data.

Usage

  • To train a model: run python train.py -c *.yaml, e.g. python train.py -c pcn.yaml
  • To test a model: run python test.py -c *.yaml, e.g. python test.py -c pcn.yaml
  • Config for each algorithm can be found in cfgs/.
  • run_train.sh and run_test.sh are provided for SLURM users.

Citation

If you find our code useful, please cite our paper:

@article{pan2021vrcnet,
  title={Variational Relational Point Completion Network},
  author={Pan, Liang and Chen, Xinyi and Cai, Zhongang and Zhang, Junzhe and Zhao, Haiyu and Yi, Shuai and Liu, Ziwei},
  journal={arXiv preprint arXiv:2104.10154},
  year={2021}
}

License

Our code is released under MIT License.

Acknowledgement

We include the following PyTorch 3rd-party libraries:
[1] ChamferDistancePytorch
[2] emd, expansion_penalty, MDS
[3] Pointnet2.PyTorch

We include the following algorithms:
[1] PCN
[2] MSN
[3] Topnet
[4] Cascade
[5] ECG
[6] VRCNet

Official implementation of the ICCV 2021 paper: "The Power of Points for Modeling Humans in Clothing".

The Power of Points for Modeling Humans in Clothing (ICCV 2021) This repository contains the official PyTorch implementation of the ICCV 2021 paper: T

Qianli Ma 158 Nov 24, 2022
Generate high quality pictures. GAN. Generative Adversarial Networks

ESRGAN generate high quality pictures. GAN. Generative Adversarial Networks """ Super-resolution of CelebA using Generative Adversarial Networks. The

Lieon 1 Dec 14, 2021
Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.

TorchSeg This project aims at providing a fast, modular reference implementation for semantic segmentation models using PyTorch. Highlights Modular De

ycszen 1.4k Jan 02, 2023
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet)

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet) (

Wei-Ting Chen 49 Dec 27, 2022
Improving Deep Network Debuggability via Sparse Decision Layers

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

Madry Lab 35 Nov 14, 2022
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021 [WIP] The code for CVPR 2021 paper 'Disentangled Cycle Consistency for H

ChongjianGE 94 Dec 11, 2022
Scene-Text-Detection-and-Recognition (Pytorch)

Scene-Text-Detection-and-Recognition (Pytorch) Competition URL: https://tbrain.t

Gi-Luen Huang 9 Jan 02, 2023
Optimus: the first large-scale pre-trained VAE language model

Optimus: the first pre-trained Big VAE language model This repository contains source code necessary to reproduce the results presented in the EMNLP 2

314 Dec 19, 2022
Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

🍐 quince Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding 🍐 Installation $ git clone

Andrew Jesson 19 Jun 23, 2022
TransVTSpotter: End-to-end Video Text Spotter with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 66 Dec 26, 2022
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab 398 Dec 30, 2022
Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions This repo contains the dataset and code for the paper Benchmarking Ro

Jiachen Sun 168 Dec 29, 2022
A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.

collie Collie is a library for preparing, training, and evaluating implicit deep learning hybrid recommender systems, named after the Border Collie do

ShopRunner 96 Dec 29, 2022
Optimizes image files by converting them to webp while also updating all references.

About Optimizes images by (re-)saving them as webp. For every file it replaced it automatically updates all references. Works on single files as well

Watermelon Wolverine 18 Dec 23, 2022
Image augmentation library in Python for machine learning.

Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independe

Marcus D. Bloice 4.8k Jan 07, 2023
Toontown: Galaxy, a new Toontown game based on Disney's Toontown Online

Toontown: Galaxy The official archive repo for Toontown: Galaxy, a new Toontown

1 Feb 15, 2022
Python package for covariance matrices manipulation and Biosignal classification with application in Brain Computer interface

pyRiemann pyRiemann is a python package for covariance matrices manipulation and classification through Riemannian geometry. The primary target is cla

447 Jan 05, 2023
A decent AI that solves daily Wordle puzzles. Works with different websites with similar wordlists,.

Wordle-AI A decent AI that solves daily "Wordle" puzzles. Works with different websites with similar wordlists. When prompted with "Word:" enter the w

Ethan 1 Feb 10, 2022
Train Dense Passage Retriever (DPR) with a single GPU

Gradient Cached Dense Passage Retrieval Gradient Cached Dense Passage Retrieval (GC-DPR) - is an extension of the original DPR library. We introduce G

Luyu Gao 92 Jan 02, 2023
Official implementation for the paper: Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Generating Smooth Pose Sequences for Diverse Human Motion Prediction This is official implementation for the paper Generating Smooth Pose Sequences fo

Wei Mao 28 Dec 10, 2022