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

Adaptive Graph Convolution for Point Cloud Analysis

Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".

Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning

RTSeg: Real-time Semantic Segmentation Comparative Study

MoViNets PyTorch implementation: Mobile Video Networks for Efficient Video Recognition;

Harmonious Textual Layout Generation over Natural Images via Deep Aesthetics Learning

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

PyTorch implementation of the wavelet analysis from Torrence & Compo

PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

Main repository for the HackBio'2021 Virtual Internship Experience for #Team-Greider ❤️

Official implementation of "A Unified Objective for Novel Class Discovery", ICCV2021 (Oral)

PED: DETR for Crowd Pedestrian Detection

Hepsiburada - Hepsiburada Urun Bilgisi Cekme

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

An LSTM for time-series classification

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

Qlib is an AI-oriented quantitative investment platform

A complete speech segmentation system using Kaldi and x-vectors for voice activity detection (VAD) and speaker diarisation.