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preprint · arXiv (Cornell University) · 2018

RenderNet: A deep convolutional network for differentiable rendering from 3D shapes

Thu Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yong‐Liang Yang

Cited by 105Open access

Abstract

Traditional computer graphics rendering pipeline is designed for procedurally generating 2D quality images from 3D shapes with high performance. The non-differentiability due to discrete operations such as visibility computation makes it hard to explicitly correlate rendering parameters and the resulting image, posing a significant challenge for inverse rendering tasks. Recent work on differentiable rendering achieves differentiability either by designing surrogate gradients for non-differentiable operations or via an approximate but differentiable renderer. These methods, however, are still limited when it comes to handling occlusion, and restricted to particular rendering effects. We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes. Spatial occlusion and shading calculation are automatically encoded in the network. Our experiments show that RenderNet can successfully learn to implement different shaders, and can be used in inverse rendering tasks to estimate shape, pose, lighting and texture from a single image.

Cite this paper

APA 7

Nguyen-Phuoc, T., Li, C., Balaban, S., & Yang, Y. (2018). RenderNet: A deep convolutional network for differentiable rendering from 3D shapes. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1806.06575

MLA 9

Nguyen-Phuoc, Thu, et al. “RenderNet: A deep convolutional network for differentiable rendering from 3D shapes.” arXiv (Cornell University), 2018, https://doi.org/10.48550/arxiv.1806.06575.

Chicago

Nguyen-Phuoc, Thu, Chuan Li, Stephen Balaban, and Yong‐Liang Yang. 2018. “RenderNet: A deep convolutional network for differentiable rendering from 3D shapes.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1806.06575.

Harvard

Nguyen-Phuoc, T. et al. (2018) ‘RenderNet: A deep convolutional network for differentiable rendering from 3D shapes’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1806.06575.

BibTeX
@misc{nguyenphuoc2018rendernet,
  title = {{RenderNet: A deep convolutional network for differentiable rendering from 3D shapes}},
  author = {Nguyen-Phuoc, Thu and Li, Chuan and Balaban, Stephen and Yang, Yong‐Liang},
  journal = {arXiv (Cornell University)},
  year = {2018},
  doi = {10.48550/arxiv.1806.06575}
}

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