Neural 3D Mesh Renderer

Hiroharu Kato, Yoshitaka Ushiku, Tatsuya Harada; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3907-3916

Abstract


For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, or rendering, involves a discrete operation called rasterization, which prevents back-propagation. Therefore, in this work, we propose an approximate gradient for rasterization that enables the integration of rendering into neural networks. Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach. Additionally, we perform gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and 3D DeepDream, with 2D supervision for the first time. These applications demonstrate the potential of the integration of a mesh renderer into neural networks and the effectiveness of our proposed renderer.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Kato_2018_CVPR,
author = {Kato, Hiroharu and Ushiku, Yoshitaka and Harada, Tatsuya},
title = {Neural 3D Mesh Renderer},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}