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Differentiable visual computing for inverse problems and machine learning

Nature Machine Intelligence volume 5, pages 1189–1199 (2023)Cite this article

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Abstract

Modern 3D computer graphics technologies are able to reproduce the dynamics and appearance of real-world environments and phenomena, building on theoretical models in applied mathematics, statistics and physics. These methods are applied in architectural design and visualization, biological imaging and visual effects. Differentiable methods, instead, aim to determine how graphics outputs (that is, the real-world dynamics or appearance) change when the environment changes. We survey this growing body of work and propose a holistic and unified differentiable visual computing pipeline. Differentiable visual computing can be leveraged to efficiently solve otherwise intractable problems in physical inference, optimal control, object detection and scene understanding, computational design, manufacturing, autonomous vehicles and robotics. Any application that can benefit from an understanding of the underlying dynamics of the real world stands to benefit substantially from a differentiable graphics treatment. We draw parallels between the well-established computer graphics pipeline and a unified differentiable graphics pipeline, targeting consumers, practitioners and researchers. The breadth of fields that these pipelines draw upon—and are of interest to—includes the physical sciences, data sciences, vision and graphics, machine learning, and adjacent mathematical and computing communities.

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Fig. 1: Forward and backward VC pipeline diagram.
Fig. 2: Forward and backward evaluation of a DVC program.
Fig. 3: Visual illustration of common surface and volumetric geometric representations.
Fig. 4: Forward and differentiable geometry processing.
Fig. 5: Forward and differentiable animation.
Fig. 6: Differentiable animation applies to a diversity of problems where system identification and unknown dynamics parameters need be inferred from (3D/4D) observations.
Fig. 7: Visualization of rasterization, ray-tracing, and path tracing algorithms.
Fig. 8: Forward and differentiable rendering.

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Acknowledgements

We thank A. Mordvintsev, A. Tagliasacchi, J. Valentin, O. Sanseviero and S. Bouaziz for early discussions on content and structure. This work was supported by a UKRI Future Leaders Fellowship (grant number G104084).

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Authors and Affiliations

  1. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Andrew Spielberg

  2. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Andrew Spielberg & Krishna Murthy Jatavallabhula

  3. Computer Science and Technology, University of Cambridge, Cambridge, UK

    Fangcheng Zhong & Cengiz Oztireli

  4. Google, Mountain View, CA, USA

    Konstantinos Rematas & Cengiz Oztireli

  5. Computer Science & Engineering, University of California — San Diego, San Diego, CA, USA

    Tzu-Mao Li

  6. Electrical & Computer Engineering, McGill University, Montréal, Québec, Canada

    Derek Nowrouzezahrai

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Spielberg, A., Zhong, F., Rematas, K. et al. Differentiable visual computing for inverse problems and machine learning. Nat Mach Intell 5, 1189–1199 (2023). https://doi.org/10.1038/s42256-023-00743-0

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