Rendu physique réaliste

Résumé

vignette|Texture de plaque métallique à losanges, représentée en gros plan via un rendu physique réaliste. Les petites abrasions donnent au métal un aspect rugueux. Une normal map est utilisée pour représenter les reliefs de la texture.Le terme rendu physique réaliste (en anglais, Physically Based Rendering, ou ) regroupe un ensemble de techniques de rendu de scène 3D, qui imitent les modèles physiques décrivant le comportement de la lumière dans le monde réel. Le rendu physique réaliste a pour but de donner aux objets virtuels une apparence proche de celle qu'ils auraient en réalité, et de baser le rendu des matériaux sur un nombre réduit de propriétés physiques qui sont peu interdépendantes, pour simplifier le travail des infographistes. L'albédo, la rugosité, et le caractère métallique sont des propriétés de matériau souvent utilisées pour ce type de rendu. Le rendu physique réaliste n'est pas uniquement utilisé pour les rendus photoréalistes et peut se prêter à des

Source officielle

https://fr.wikipedia.org/wiki/Rendu_physique_r%C3%A9aliste

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Differentiable Signed Distance Function Rendering

Wenzel Alban Jakob, Sébastien Nicolas Speierer, Delio Aleardo Vicini

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also poses severe challenges: shapes are intertwined with visibility, whose discontinuous nature introduces severe bias in computed derivatives unless costly precautions are taken. Shape representations like triangle meshes suffer from additional difficulties, since the continuous optimization of mesh parameters cannot introduce topological changes. One common solution to these difficulties entails representing shapes using signed distance functions (SDFs) and gradually adapting their zero level set during optimization. Previous differentiable rendering of SDFs did not fully account for visibility gradients and required the use of mask or silhouette supervision, or discretization into a triangle mesh. In this article, we show how to extend the commonly used sphere tracing algorithm so that it additionally outputs a reparameterization that provides the means to compute accurate shape parameter derivatives. At a high level, this resembles techniques for differentiable mesh rendering, though we show that the SDF representation admits a particularly efficient reparameterization that outperforms prior work. Our experiments demonstrate the reconstruction of (synthetic) objects without complex regularization or priors, using only a per-pixel RGB loss.

ASSOC COMPUTING MACHINERY2022

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Path Replay Backpropagation: Differentiating Light Paths using Constant Memory and Linear Time

Wenzel Alban Jakob, Sébastien Nicolas Speierer, Delio Aleardo Vicini

Differentiable physically-based rendering has become an indispensable tool for solving inverse problems involving light. Most applications in this area jointly optimize a large set of scene parameters to minimize an objective function, in which case reverse-mode differentiation is the method of choice for obtaining parameter gradients. However, existing techniques that perform the necessary differentiation step suffer from either statistical bias or a prohibitive cost in terms of memory and computation time. For example, standard techniques for automatic differentiation based on program transformation or Wengert tapes lead to impracticably large memory usage when applied to physically-based rendering algorithms. A recently proposed adjoint method by Nimier-David et al. [2020] reduces this to a constant memory footprint, but the computation time for unbiased gradient estimates then becomes quadratic in the number of scattering events along a light path. This is problematic when the scene contains highly scattering materials like participating media. In this paper, we propose a new unbiased backpropagation algorithm for rendering that only requires constant memory, and whose computation time is linear in the number of scattering events (i.e., just like path tracing). Our approach builds on the invertibility of the local Jacobian at scattering interactions to recover the various quantities needed for reverse-mode differentiation. Our method also extends to specular materials such as smooth dielectrics and conductors that cannot be handled by prior work.

ASSOC COMPUTING MACHINERY2021

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Woven fabrics have a wide range of appearance determined by their small-scale 3D structure. Accurately modeling this structural detail can produce highly realistic renderings of fabrics and is critical for predictive rendering of fabric appearance. But building these yarnlevel volumetric models is challenging. Procedural techniques are manually intensive, and fail to capture the naturally arising irregularities which contribute significantly to the overall appearance of cloth. Techniques that acquire the detailed 3D structure of real fabric samples are constrained only to model the scanned samples and cannot represent different fabric designs. This paper presents a new approach to creating volumetric models of woven cloth, which starts with user-specified fabric designs and produces models that correctly capture the yarn-level structural details of cloth. We create a small database of volumetric exemplars by scanning fabric samples with simple weave structures. To build an output model, our method synthesizes a new volume by copying data from the exemplars at each yarn crossing to match a weave pattern that specifies the desired output structure. Our results demonstrate that our approach generalizes well to complex designs and can produce highly realistic results at both large and small scales. © 2012 ACM 0730-0301/2012/08-ART75.

ACM2012

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