# Differentiable Visual Computing

arXiv: https://arxiv.org/abs/1904.12228 · HTML: https://mlsanity.com/paper/1904.12228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Differentiable Rendering of Neural SDFs through Reparameterization](https://mlsanity.com/paper/2206.05344/) (2022)
- [Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction](https://mlsanity.com/paper/2203.12613/) (2022)
- [Physical world assistive signals for deep neural network classifiers -- neither defense nor attack](https://mlsanity.com/paper/2105.00622/) (2021)

## Abstract (arXiv)

Derivatives of computer graphics, image processing, and deep learning algorithms have tremendous use in guiding parameter space searches, or solving inverse problems. As the algorithms become more sophisticated, we no longer only need to differentiate simple mathematical functions, but have to deal with general programs which encode complex transformations of data. This dissertation introduces three tools for addressing the challenges that arise when obtaining and applying the derivatives for complex graphics algorithms. Traditionally, practitioners have been constrained to composing programs with a limited set of operators, or hand-deriving derivatives. We extend the image processing language Halide with reverse-mode automatic differentiation, and the ability to automatically optimize the gradient computations. This enables automatic generation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort. In 3D rendering, the gradient is required with respect to variables such as camera parameters, geometry, and appearance. However, computing the gradient is challenging because the rendering integral includes visibility terms that are not differentiable. We introduce, to our knowledge, the first general-purpose differentiable ray tracer that solves the full rendering equation, while correctly taking the geometric discontinuities into account. Finally, we demonstrate that the derivatives of light path throughput can also be useful for guiding sampling in forward rendering. Simulating light transport in the presence of multi-bounce glossy effects and motion in 3D rendering is challenging due to the hard-to-sample high-contribution areas. We present a Markov Chain Monte Carlo rendering algorithm that extends Metropolis Light Transport by automatically and explicitly adapting to the local integrand, thereby increasing sampling efficiency.