Differentiable Monte Carlo Ray Tracing Through Edge Sampling

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • MIT CSAIL

Abstract

Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates.

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision.

We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks.

Details

Original languageEnglish
Article number222
Pages (from-to)1-11
JournalACM Transactions on Graphics
Volume37
Issue number6
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed
EventACM International Conference and Exhibition on Computer Graphics and Interactive Techniques
- Tokyo, Japan
Duration: 4 Dec 20187 Dec 2018
Conference number: 11

    Research areas

  • computer graphics, inverse rendering, ray tracing

ID: 30789573