This is the source code of Self-Supervised 3D Mesh Reconstruction From Single Images (CVPR-2021).
- Linux
- Python >= 3.6
- CUDA >= 10.0.130 (with
nvccinstalled)
$ conda create --name smr python=3.7
$ conda activate smrYou can directly install the requirements through:
$ pip install -r requirements.txtYou can also install the required packages seperately
-
Pytorch (Please first check your cuda version)
$ conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
-
Kaolin Library
$ git clone --recursive https://github.com/NVIDIAGameWorks/kaolin $ git checkout v0.9.1 $ python setup.py develop
-
Others: tqdm, trimesh, imageio, tensorboard.
-
Dataset
Download the processed data from Google Drive.
-
Run
DATA_ROOT=/path/to/Bird/ $ python train.py --imageSize 128 \ --batchSize 24 \ --lr 0.0001 \ --niter 500 \ --dataroot $DATA_ROOT \ --template_path ./template/sphere.obj \ --outf ./log/Bird/SMR \ --azi_scope 360 \ --elev_range '0~30' \ --dist_range '2~6' \ --lambda_gan 0.0001 \ --lambda_reg 1.0 \ --lambda_data 1.0 \ --lambda_ic 0.1 \ --lambda_lc 0.001or Multi-GPU
python train.py --imageSize 256 \ --batchSize 24 \ --lr 0.0001 \ --niter 500 \ --dataroot $DATA_ROOT \ --template_path ./template/sphere.obj \ --outf ./log/Bird/SMR_256_2gpus_flip\ --azi_scope 360 \ --elev_range '0~30' \ --dist_range '2~6' \ --lambda_gan 0.0001 \ --lambda_reg 1.0 \ --lambda_data 1.0 \ --lambda_ic 0.1 \ --lambda_lc 0.001 \ --multigpus
Tao Hu - taohu@cse.cuhk.edu.hk
@InProceedings{Hu_2021_CVPR,
author = {Hu, Tao and Wang, Liwei and Xu, Xiaogang and Liu, Shu and Jia, Jiaya},
title = {Self-Supervised 3D Mesh Reconstruction From Single Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {6002-6011}
}