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).

Pytorch implementation for "Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter".

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter This is a pytorch-based implementation for paper Implicit Feature Alignme

wangtianwei 61 Nov 12, 2022
A PyTorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

A PyTorch implementation of V-Net Vnet is a PyTorch implementation of the paper V-Net: Fully Convolutional Neural Networks for Volumetric Medical Imag

Matthew Macy 606 Dec 21, 2022
TF Image Segmentation: Image Segmentation framework

TF Image Segmentation: Image Segmentation framework The aim of the TF Image Segmentation framework is to provide/provide a simplified way for: Convert

Daniil Pakhomov 546 Dec 17, 2022
COIN the currently largest dataset for comprehensive instruction video analysis.

COIN Dataset COIN is the currently largest dataset for comprehensive instruction video analysis. It contains 11,827 videos of 180 different tasks (i.e

86 Dec 28, 2022
Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image

Ashish Sinha 394 Dec 28, 2022
Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Geotrend 79 Dec 28, 2022
一些经典的CTR算法的复现; LR, FM, FFM, AFM, DeepFM,xDeepFM, PNN, DCN, DCNv2, DIFM, AutoInt, FiBiNet,AFN,ONN,DIN, DIEN ... (pytorch, tf2.0)

CTR Algorithm 根据论文, 博客, 知乎等方式学习一些CTR相关的算法 理解原理并自己动手来实现一遍 pytorch & tf2.0 保持一颗学徒的心! Schedule Model pytorch tensorflow2.0 paper LR ✔️ ✔️ \ FM ✔️ ✔️ Fac

luo han 149 Dec 20, 2022
a short visualisation script for pyvideo data

PyVideo Speakers A CLI that visualises repeat speakers from events listed in https://github.com/pyvideo/data Not terribly efficient, but you know. Ins

Katie McLaughlin 3 Nov 24, 2021
This repository is the code of the paper "Sparse Spatial Transformers for Few-Shot Learning".

🌟 Sparse Spatial Transformers for Few-Shot Learning This code implements the Sparse Spatial Transformers for Few-Shot Learning(SSFormers). Our code i

chx_nju 38 Dec 13, 2022
Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman*, Gabri

224 Dec 29, 2022
Recurrent Variational Autoencoder that generates sequential data implemented with pytorch

Pytorch Recurrent Variational Autoencoder Model: This is the implementation of Samuel Bowman's Generating Sentences from a Continuous Space with Kim's

Daniil Gavrilov 347 Nov 14, 2022
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algori

Anish 324 Dec 27, 2022
Simple tools for logging and visualizing, loading and training

TNT TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is desi

1.5k Jan 02, 2023
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

Temporally Efficient Vision Transformer for Video Instance Segmentation Temporally Efficient Vision Transformer for Video Instance Segmentation (CVPR

Hust Visual Learning Team 203 Dec 31, 2022
Official Python implementation of the FuzionCoin protocol

PyFuzc Official Python implementation of the FuzionCoin protocol WARNING: Under construction. Use at your own risk. Some functions may not work. Setup

FuzionCoin 3 Jul 07, 2022
Official PyTorch implementation of the paper: DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample

DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample (ICCV 2021 Oral) Project | Paper Official PyTorch implementation of the pape

Eliahu Horwitz 393 Dec 22, 2022
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
unofficial pytorch implement of "Squareplus: A Softplus-Like Algebraic Rectifier"

SquarePlus (Pytorch implement) unofficial pytorch implement of "Squareplus: A Softplus-Like Algebraic Rectifier" SquarePlus Squareplus is a Softplus-L

SeeFun 3 Dec 29, 2021
PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

PASTRIE Official release of the corpus described in the paper: Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, and Nathan Schn

NERT @ Georgetown 4 Dec 02, 2021
2021:"Bridging Global Context Interactions for High-Fidelity Image Completion"

TFill arXiv | Project This repository implements the training, testing and editing tools for "Bridging Global Context Interactions for High-Fidelity I

Chuanxia Zheng 111 Jan 08, 2023