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Hiroharu Kato
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Readme.md

Differentiable Ray Sampling for Neural 3D Representation

This software is developed as a part of Preferred Networks 2019 Research Internships.
The main developer is N. H. Shimada.

The code realizes a differentiable renderer for neural 3D representation. The prediction model can be trained via only 2D images and conducts 3D reconstruction from a single RGB image (as below).
For more details, please refer to slides and blog (Japanese).

Teaser Image

Pre-trained models

Please check out the jupyter notebook 1 which shows the qualititive results using the trained models in the case of car class.

Training and Evaluating

There must be -

  • Creating the dataset of 10 rendered images of each car from ShapeNet V1 using blender.
    (Please refer to the paper and codes of Tulsiani+ CVPR 2017)

Run the main code

python3 DRS/Main.py 0
to train networks and save them into DRS/save/.

Evaluating models

LICENSE

MIT License (see the LICENSE file for details).

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