Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Last update: Aug 17, 2022

Related tags

Deep Learning view_prior_learning

Overview

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

This is code for a paper Learning View Priors for Single-view 3D Reconstruction by Hiroharu Kato and Tatsuya Harada.

For more details, please visit project page.

Environment

This code is tested on Python 2.7.

Testing pretrained models

Download datasets and pretrained models from here and extract them under data directory. This can be done by the following commands.

mkdir data
cd data
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1G5gelwQGniwGgyG92ls_dfc1VtLUiM3s' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1G5gelwQGniwGgyG92ls_dfc1VtLUiM3s" -O dataset.zip && rm -rf /tmp/cookies.txt
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=119D78nZ329J90yTkfSrq4imRuQ8ON5N_' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=119D78nZ329J90yTkfSrq4imRuQ8ON5N_" -O models.zip && rm -rf /tmp/cookies.txt
unzip dataset.zip
unzip models.zip
cd ../

Quantitative evaluation of our best model on ShapeNet dataset is done by the following command.

python ./mesh_reconstruction/test.py -ds shapenet -nt 0 -eid shapenet_multi_color_nv20_uvr_cc_long

This outputs

02691156 0.691549002544
02828884 0.59788288686
02933112 0.720973934558
02958343 0.804359183654
03001627 0.603543199669
03211117 0.593105481352
03636649 0.502730883482
03691459 0.673864365473
04090263 0.664089877796
04256520 0.654773500288
04379243 0.602735843742
04401088 0.767574659204
04530566 0.616663414002
all 0.653372787125

Other ShapeNet models are listed in test_shapenet.sh.

Drawing animated gif of ShapeNet reconstruction requires the dataset provided by [Kar et al. NIPS 2017].

cd data
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=17GjULuQZsn-s92PQFQSBzezDkonowIxR' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=17GjULuQZsn-s92PQFQSBzezDkonowIxR" -O lsm.tar.gz && rm -rf /tmp/cookies.txt
tar xvzf lsm.tar.gz
cd shapenet_release/renders/
find ./ -name "*.tar.gz" -exec tar xvzf {} \;
cd ../../../

Then, the following commands

mkdir tmp
bash make_gif.sh

output the following images.

Training

Training requires pre-trained AlexNet model.

cd data
mkdir caffemodel
cd caffemodel
wget http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
cd ../../

Training of the provided pre-trained models is done by

bash train_shapenet.sh
bash train_pascal.sh

Citation

@InProceedings{kato2019vpl,
    title={Learning View Priors for Single-view 3D Reconstruction},
    author={Hiroharu Kato and Tatsuya Harada},
    booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2019}
}

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Related tags

Overview

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Environment

Testing pretrained models

Training

Citation

Owner

Hiroharu Kato

Train a deep learning net with OpenStreetMap features and satellite imagery.

Bayesian Neural Networks in PyTorch

Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021)

Vit-ImageClassification - Pytorch ViT for Image classification on the CIFAR10 dataset

ML models implementation practice

An automated facial recognition based attendance system (desktop application)

project page for VinVL

ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Mercury: easily convert Python notebook to web app and share with others

Music library streaming app written in Flask & VueJS

D2LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

Network Enhancement implementation in pytorch

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

Large dataset storage format for Pytorch

AnimationKit: AI Upscaling & Interpolation using Real-ESRGAN+RIFE

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Robust Lane Detection via Expanded Self Attention (WACV 2022)

Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces