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arXiv:2006.12057cs.CV

Differentiable Rendering: A Survey

Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, Adrien Gaidon

Abstract

Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation. Despite their success, they generally lack the understanding of 3D objects which form the image, as it is not always possible to collect 3D information about the scene or to easily annotate it. Differentiable rendering is a novel field which allows the gradients of 3D objects to be calculated and propagated through images. It also reduces the requirement of 3D data collection and annotation, while enabling higher success rate in various applications. This paper reviews existing literature and discusses the current state of differentiable rendering, its applications and open research problems.

arXiv comments
Journal reference
DOI
Subjects
cs.CVComputer Vision and Pattern Recognition cs.GRGraphics

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from May 2022 to Nov 2022.

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Discussed in comments2 threads

  1. Fuzzy Metaballs: Approximate differentiable rendering with algebraic surfaces 1 comment

    • Actually, that is the correct definition! It might sound unintuitive at first, but I think this paper[1] describes it really well (p.1): > The last years have clearly shown that neural networks are effective for 2D and 3D reasoning. However, most 3D estimation methods rely on…

      Timon3View on HNParent context

  2. Kornia: Differential Computer Vision 1 comment

    • In case you're asking about the parent comment (s/Differential/Differentiable), it means the title should be "differentiable CV" (instead of "differential CV"). "Differentiable" describes some computation for which derivatives can be computed. "Differential" is a more general…

      andersourceView on HNParent context

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