SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images

Overview

SymmetryNet

SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images

ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2020)

Created by Yifei Shi, Junwen Huang, Hongjia Zhang, Xin Xu, Szymon Rusinkiewicz and Kai Xu

teaser

This repository includes:

  • tools: the training scripts and evaluation scripts
    • tools/train_shapenet.py: the training script for shapenet dataset
    • tools/train_ycb.py: the training script for ycb dataset
    • tools/train_scannet.py: the training script for scannet dataset
    • tools/evaluation: the evaluation scripts
      • evaluation/eval_ref_shapenet.py: the evaluation script for reflectional symmetry on shapenet dataset
      • evaluation/eval_ref_ycb.py: the evaluation script for reflectional symmetry on ycb dataset
      • evaluation/eval_ref_scannet.py: the evaluation script for reflectional symmetry on scannet dataset
      • evaluation/eval_rot_shapenet.py: the evaluation script for rotational symmetry on shapenet dataset
      • evaluation/eval_rot_ycb.py: the evaluation script for rotational symmetry on ycb dataset
      • evaluation/eval_rot_scannet.py: the evaluation script for rotational symmetry on scannet dataset
  • lib: the core Python library for networks and loss
    • lib/loss.py: symmetrynet loss caculation for both reflectional and rotational symmetries,the loss items are listed at the end of the text
    • lib/network.py: network architecture
    • lib/tools.py: functions for the operation of rotation and reflection
    • lib/verification.py: verification of the rotational and reflectional symmetries
  • datasets: the dataloader and training/testing lists
    • datasets/shapenet/dataset.py: the training dataloader for shapnet dataset
    • datasets/shapenet/dataset_eval.py: the evaluation dataloader for shapnet dataset
      • datasets/shapenet/dataset_config/*.txt: training and testing splits for shapenet dataset, the testing splits includ holdout view/instance/category
    • datasets/ycb/dataset.py: the training dataloader for ycb dataset
    • datasets/ycb/dataset_eval.py: the evaluation dataloader for ycb dataset
      • datasets/ycb/dataset_config/*.txt: training and testing splits for shapenet dataset,the training/testing splits fallow the ycb defult settings
    • datasets/shapenet/dataset.py: the training dataloader for scannet dataset
    • datasets/shapenet/dataset_eval.py: the evaluation dataloader for scannet dataset
      • datasets/scannet/dataset_config/*.txt: training and testing splits for scannet dataset,the testing splits includ holdout view/scene

Environments

pytorch>=0.4.1 python >=3.6

Datasets

  • ShapeNet dataset

    • shapenetcore: this folder saves the models and their ground truth symmetries for each instance
    • rendered_data: this folder saves the rgbd images that we rendered for each instance, including their ground truth pose and camera intrinsic matrix, etc.
    • name_list.txt: this file saves the correspondence between the name of instances and their ID in this project(the names are too long to identify)
  • YCB dataset

    • models: this folder saves the ground truth model symmetry for each instance
    • data: this folder saves the rgbd videos and the ground truth poses and camera information
    • classes.txt: this file saves the correspondence between the name of YCB objects and their *.xyz models
    • symmetries.txt: this file saves all the ground truth symmetries for ycb object models

Training

To train the network with the default parameter on shapenet dataset, run

python tools/train_shapenet.py --dataset_root= your/folder/to/shapnet/dataset

To train the network with the default parameter on ycb dataset, run

python tools/train_ycb.py --dataset_root= your/folder/to/ycb/dataset

To train the network with the default parameter on scannet dataset, run

python tools/train_scannet.py --dataset_root= your/folder/to/scannet/dataset

Evaluation

To evaluate the model with our metric on shapenet, for reflectional symmetry, run

python tools/evaluation/eval_ref_shapenet.py

for rotational symmetry, run

python tools/evaluation/eval_rot_shapenet.py

To evaluate the model with our metric on ycb, for reflectional symmetry, run

python tools/evaluation/eval_ref_ycb.py

for rotational symmetry, run

python tools/evaluation/eval_rot_ycb.py

To evaluate the model with our metric on scannet, for reflectional symmetry, run

python tools/evaluation/eval_ref_scannet.py

for rotational symmetry, run

python tools/evaluation/eval_rot_scannet.py

Pretrained model & data download

The pretrained models and data can be found at here (dropbox) and here (baidu yunpan, password: symm).

The implement of papar "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization"

SIGIR2021-EGLN The implement of paper "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization" Neural graph based Col

15 Dec 27, 2022
Neural Oblivious Decision Ensembles

Neural Oblivious Decision Ensembles A supplementary code for anonymous ICLR 2020 submission. What does it do? It learns deep ensembles of oblivious di

25 Sep 21, 2022
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

Kentaro Wada 218 Oct 27, 2022
Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

FedLearn-algo Installation Development Environment Checklist python3 (3.6 or 3.7) is required. To configure and check the development environment is c

89 Nov 14, 2022
Voxel Transformer for 3D object detection

Voxel Transformer This is a reproduced repo of Voxel Transformer for 3D object detection. The code is mainly based on OpenPCDet. Introduction We provi

173 Dec 25, 2022
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)

Improved Techniques for Training Score-Based Generative Models This repo contains the official implementation for the paper Improved Techniques for Tr

174 Dec 26, 2022
Curved Projection Reformation

Description Assuming that we already know the image of the centerline, we want the lumen to be displayed on a plane, which requires curved projection

夜听残荷 5 Sep 11, 2022
[WWW 2022] Zero-Shot Stance Detection via Contrastive Learning

PT-HCL for Zero-Shot Stance Detection The code of this repository is constantly being updated... Please look forward to it! Introduction This reposito

Akuchi 12 Dec 21, 2022
Official implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform This repository is the implementation of "Variable-Rate Deep Image C

Myungseo Song 47 Dec 13, 2022
End-To-End Memory Network using Tensorflow

MemN2N Implementation of End-To-End Memory Networks with sklearn-like interface using Tensorflow. Tasks are from the bAbl dataset. Get Started git clo

Dominique Luna 339 Oct 27, 2022
TimeSHAP explains Recurrent Neural Network predictions.

TimeSHAP TimeSHAP is a model-agnostic, recurrent explainer that builds upon KernelSHAP and extends it to the sequential domain. TimeSHAP computes even

Feedzai 90 Dec 18, 2022
Implementation of the "PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences" paper.

PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences Introduction Point cloud sequences are irregular and unordered in the spatial dimen

Hehe Fan 63 Dec 09, 2022
Pytorch Implementation of "Diagonal Attention and Style-based GAN for Content-Style disentanglement in image generation and translation" (ICCV 2021)

DiagonalGAN Official Pytorch Implementation of "Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Trans

32 Dec 06, 2022
Pytorch implementation of our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION.

LiMuSE Overview Pytorch implementation of our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION. LiMuSE explores group communication on a multi

Auditory Model and Cognitive Computing Lab 17 Oct 26, 2022
TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1).

M1-tensorflow-benchmark TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1). I was initially testing if Tens

particle 2 Jan 05, 2022
Unsupervised captioning - Code for Unsupervised Image Captioning

Unsupervised Image Captioning by Yang Feng, Lin Ma, Wei Liu, and Jiebo Luo Introduction Most image captioning models are trained using paired image-se

Yang Feng 207 Dec 24, 2022
《Lerning n Intrinsic Grment Spce for Interctive Authoring of Grment Animtion》

Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation Overview This is the demo code for training a motion invariant enco

YuanBo 213 Dec 14, 2022
Numerai tournament example scripts using NN and optuna

numerai_NN_example Numerai tournament example scripts using pytorch NN, lightGBM and optuna https://numer.ai/tournament Performance of my model based

Takahiro Maeda 12 Oct 10, 2022
Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds

Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng Prerequisites We have tested the code on Ubun

41 Dec 12, 2022
modelvshuman is a Python library to benchmark the gap between human and machine vision

modelvshuman is a Python library to benchmark the gap between human and machine vision. Using this library, both PyTorch and TensorFlow models can be evaluated on 17 out-of-distribution datasets with

Bethge Lab 244 Jan 03, 2023