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1. PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting

   Zhang, Kai; Luan, Fuju; Wang, Qianqian; Bala, Kavita; Snavely, Noah (July 2021, IEEE Conference on Computer Vision and Pattern Recognition)

   null (Ed.)

   We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images. Our framework represents specular BRDFs and environmental illumination using mix- tures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our re- constructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination. 

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2. The Role of Subsurface Scattering in Glossiness Perception

   https://doi.org/10.1145/3458438  

   Gigilashvili, Davit; Shi, Weiqi; Wang, Zeyu; Pedersen, Marius; Hardeberg, Jon_Yngve; Rushmeier, Holly (May 2021, ACM Transactions on Applied Perception)

   This study investigates the potential impact of subsurface light transport on gloss perception for the purposes of broadening our understanding of visual appearance in computer graphics applications. Gloss is an important attribute for characterizing material appearance. We hypothesize that subsurface scattering of light impacts the glossiness perception. However, gloss has been traditionally studied as a surface-related quality and the findings in the state-of-the-art are usually based on fully opaque materials, although the visual cues of glossiness can be impacted by light transmission as well. To address this gap and to test our hypothesis, we conducted psychophysical experiments and found that subjects are able to tell the difference in terms of gloss between stimuli that differ in subsurface light transport but have identical surface qualities and object shape. This gives us a clear indication that subsurface light transport contributes to a glossy appearance. Furthermore, we conducted additional experiments and found that the contribution of subsurface scattering to gloss varies across different shapes and levels of surface roughness. We argue that future research on gloss should include transparent and translucent media and to extend the perceptual models currently limited to surface scattering to more general ones inclusive of subsurface light transport. 

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3. Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model

   Sun, Luning; Han, Xu; Gao, Han; Wang, Jian-Xun; Liu, Li-Ping (December 2023, Advances in Neural Information Processing Systems 36)

   Accurate prediction of dynamical systems in unstructured meshes has recently shown successes in scientific simulations. Many dynamical systems have a nonnegligible level of stochasticity introduced by various factors (e.g. chaoticity), so there is a need for a unified framework that captures both deterministic and stochastic components in the rollouts of these systems. Inspired by regeneration learning, we propose a new model that combines generative and sequential networks to model dynamical systems. Specifically, we use an autoencoder to learn compact representations of full-space physical variables in a low-dimensional space. We then integrate a transformer with a conditional normalizing flow model to model the temporal sequence of latent representations. We evaluate the new model in both deterministic and stochastic systems. The model outperforms several competitive baseline models and makes more accurate predictions of deterministic systems. Its own prediction error is also reflected in its uncertainty estimations. When predicting stochastic systems, the proposed model generates high-quality rollout samples. The mean and variance of these samples well match the statistics of samples computed from expensive numerical simulations. 

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4. NeuPreSS: Compact Neural Precomputed Subsurface Scattering for Distant Lighting of Heterogeneous Translucent Objects

   https://doi.org/10.1111/cgf.15234  

   TG, T; Frisvad, J R; Ramamoorthi, R; Jensen, H W (October 2024, Computer Graphics Forum)

   Abstract Monte Carlo rendering of translucent objects with heterogeneous scattering properties is often expensive both in terms of memory and computation. If the scattering properties are described by a 3D texture, memory consumption is high. If we do path tracing and use a high dynamic range lighting environment, the computational cost of the rendering can easily become significant. We propose a compact and efficient neural method for representing and rendering the appearance of heterogeneous translucent objects. Instead of assuming only surface variation of optical properties, our method represents the appearance of a full object taking its geometry and volumetric heterogeneities into account. This is similar to a neural radiance field, but our representation works for an arbitrary distant lighting environment. In a sense, we present a version of neural precomputed radiance transfer that captures relighting of heterogeneous translucent objects. We use a multi‐layer perceptron (MLP) with skip connections to represent the appearance of an object as a function of spatial position, direction of observation, and direction of incidence. The latter is considered a directional light incident across the entire non‐self‐shadowed part of the object. We demonstrate the ability of our method to compactly store highly complex materials while having high accuracy when comparing to reference images of the represented object in unseen lighting environments. As compared with path tracing of a heterogeneous light scattering volume behind a refractive interface, our method more easily enables importance sampling of the directions of incidence and can be integrated into existing rendering frameworks while achieving interactive frame rates. 

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5. Errors in visual search: Are they stochastic or deterministic?

   https://doi.org/10.1186/s41235-024-00543-z  

   Li, Aoqi; Hulleman, Johan; Wolfe, Jeremy M (December 2024, Cognitive Research: Principles and Implications)

   Abstract In any visual search task in the lab or in the world, observers will make errors. Those errors can be categorized as “deterministic”: If you miss this target in this display once, you will definitely miss it again. Alternatively, errors can be “stochastic”, occurring randomly with some probability from trial to trial. Researchers and practitioners have sought to reduce errors in visual search, but different types of errors might require different techniques for mitigation. To empirically categorize errors in a simple search task, our observers searched for the letter “T” among “L” distractors, with each display presented twice. When the letters were clearly visible (white letters on a gray background), the errors were almost completely stochastic (Exp 1). An error made on the first appearance of a display did not predict that an error would be made on the second appearance. When the visibility of the letters was manipulated (letters of different gray levels on a noisy background), the errors became a mix of stochastic and deterministic. Unsurprisingly, lower contrast targets produced more deterministic errors. (Exp 2). Using the stimuli of Exp 2, we tested whether errors could be reduced using cues that guided attention around the display but knew nothing about the content of that display (Exp3a, b). This had no effect, but cueing all item locations did succeed in reducing deterministic errors (Exp3c). 

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