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Self-supervised Single-view 3D Reconstruction via Semantic Consistency

Self-supervised Single-view 3D Reconstruction via Semantic Consistency

UMR

Self-supervised Single-view 3D Reconstruction.

teaser

Citation

Please cite our paper if you find this code useful for your research.

@inproceedings{umr2020,
  title={Self-supervised Single-view 3D Reconstruction via Semantic Consistency},
  author={Li, Xueting and Liu, Sifei and Kim, Kihwan and De Mello, Shalini and Jampani, Varun and Yang, Ming-Hsuan and Kautz, Jan},
  booktitle={ECCV},
  year={2020}
}

Prerequisites

  • Download code & pre-trained model:

    Git clone the code by:

    git clone https://github.com/NVlabs/UMR $ROOTPATH
    

    The pre-trained weights and semantic template can be downloaded from Google drive and unzip at $ROOTPATH/UMR/.

  • Install packages:

    Virtual environments are not required but highly recommended:

    conda create -n umr python=3.6
    source activate umr
    

    Then run:

    cd $ROOTPATH/UMR
    sh install.sh
    

Run the demo

Run the following command from the $ROOTPATH directory:

python -m UMR.experiments.demo --model_path UMR/cachedir/snapshots/cub_net/pred_net_latest.pth --img_path UMR/demo_imgs/birdie.jpg --out_path UMR/cachedir/demo

The results will be saved at out_path in the above command.

Quantitative Evaluation

Download CUB bird images by:

wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz && tar -xf CUB_200_2011.tgz

Command for keypoint transfer evaluation using predicted texture flow:

python -m UMR.experiments.test_kp --model_path UMR/cachedir/snapshots/cub_net/pred_net_latest.pth --split test --number_pairs 10000 --cub_cache_dir UMR/cachedir/cub/ --cub_dir CUB_200_2011/

Command for keypoint transfer evaluation using predicted camera pose:

python -m UMR.experiments.test_kp --model_path UMR/cachedir/snapshots/cub_net/pred_net_latest.pth --split test --number_pairs 10000 --mode cam --cub_cache_dir UMR/cachedir/cub/ --cub_dir CUB_200_2011/

For keypoint transfer result visualization, add --visualize. The visualizations will be saved at $ROOTPATH/UMR/cachedir/results_vis/

Command for evaluating mask IoU:

python -m UMR.experiments.test_iou --model_path UMR/cachedir/snapshots/cub_net/pred_net_latest.pth --split test --cub_cache_dir UMR/cachedir/cub/ --cub_dir CUB_200_2011/ --batch_size 32

Training

To train the full model with semantic consistency constraint:

  • Prepare SCOPS predictions following instructions here.

  • Download semantic template as described above if haven't done so.

  • Run the following command from the $ROOTPATH folder:

    python -m UMR.experiments.train_s2 --name=cub_s2 --batch_size 16 --cub_dir CUB_200_2011/ --cub_cache_dir UMR/cachedir/cub --scops_path SCOPS/results/cub/ITER_60000/train/dcrf_prob/ --stemp_path UMR/cachedir/cub/scops/
    

    If the code is ran on multiple GPUs, add --multi_gpu --gpu_num GPU_NUMBER to the above commands.

If you wish to train from scratch (i.e. learn the semantic template from scratch):

  • Run the following command from the $ROOTPATH folder:

    python -m UMR.experiments.train_s1 --name=cub_s1 --gpu_num 4 --multi_gpu -cub_dir CUB_200_2011/ --cub_cache_dir UMR/cachedir/cub
    
  • Then compute the semantic template by:

    python -m UMR.experiments.avg_uv --model_path UMR/cachedir/snapshots/cub_s1/pred_net_latest.pth --batch_size 16 --out_dir UMR/cachedir/snapshots/cub_s1 --cub_dir CUB_200_2011/ --cub_cache_dir UMR/cachedir/cub --scops_path SCOPS/results/cub/ITER_60000/train/dcrf_prob/
    

    Now you can use the computed semantic template in the model training by setting stemp_path in train_s2.py to out_dir in the above command.

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