Recent advances in cloud rendering have brought us a promising alternative for interactive photorealistic rendering on lightweight devices, which used to be only available on high-end platforms equipped with powerful graphic cards. This technique enables users to perform rendering-related creative tasks, such as 3D product visualization and lighting design, from the comfort of any location using handheld devices, rather than being confined to the front of a noisy heat-generating workstation. However, existing large-scale cloud rendering systems that stream path-traced frames from the server to the client present extremely high rendering costs and transmission bandwidth requirements, even with advanced path-tracing acceleration and video compression techniques. To alleviate these problems, we propose a novel streaming-aware rendering framework that is able to learn a joint optimal model integrating two path-tracing acceleration techniques (adaptive sampling and denoising) and video compression technique. Our joint model can fully exploit the inherent connections between these techniques and thus achieve substantially reduced rendering costs and enhanced compression quality. We also introduce the collaboration of client rendering ability to assist the frame decoding by rendering G-buffers as the shared side information. We demonstrate that appropriately incorporating the geometry and material priors from G-buffers into a neural compression pipeline can significantly reduce the streaming bandwidth in a cloud rendering system, and lighten the compression module design for computation efficiency. Our experiments show that our method delivers the best quality at various bitrates compared to existing Monte Carlo rendering streaming schemes, while remaining lightweight and efficient for cross-platform thin clients, including mobiles and tablets.

Real-time subsurface scattering techniques are widely used in translucent material rendering. Among advanced methods that rely on the bidirectional scattering-surface reflectance distribution function (BSSRDF), screen space algorithms exhibit limited translucency, while existing large-distance methods are inefficient and yield poor illumination details. To address these limitations for better large-distance scattering, we develop a novel algorithm by extending the photon beam diffusion (PBD) model within the light view and screen space. Unlike surface irradiance in prior methods, we incorporate the refracted beam in the medium into real-time scattering estimation, presenting a new consideration for photon beam utilization. Concretely, we store all photon beam samples in light view textures and utilize an adaptive sampling pattern for beam sample selection in large filtering kernel sizes. This can reduce the sample count based on surface attributes. In screen space, virtual sources are derived from samples to estimate PBD contributions, with an approximation that preserves boundary conditions. To avoid possible overestimation, we implement correction factors that scale contributions, effectively aligning our results with path-tracing references. Through these reformulations, our efficient PBD generates results closest to references among existing methods. The experiments accurately represent better front-face illumination details and backlit translucency effects, while significantly accelerating performance compared to previous large-distance methods.

Abstract Sub‐surface scattering is always an important feature in translucent material rendering. When light travels through optically thick media, its transport within the medium can be approximated using diffusion theory, and is appropriately described by the bidirectional scattering‐surface reflectance distribution function (BSSRDF). BSSRDF methods rely on assumptions about object geometry and light distribution in the medium, which limits their applicability to general participating media problems. However, despite the high computational cost of path tracing, BSSRDF methods are often favoured due to their suitability for real‐time applications. We review these methods and discuss the most recent breakthroughs in this field. We begin by summarizing various BSSRDF models and then implement most of them in a 2D searchlight problem to demonstrate their differences. We focus on acceleration methods using BSSRDF, which we categorize into two primary groups: pre‐computation and texture methods. Then we go through some related topics, including applications and advanced areas where BSSRDF is used, as well as problems that are sometimes important yet are ignored in sub‐surface scattering estimation. In the end of this survey, we point out remaining constraints and challenges, which may motivate future work to facilitate sub‐surface scattering.