Obscurance and Ambient Occlusion (AO) are popular techniques in both film and games that model how ambient light is shadowed. While it is largely a solved problem for static scenes, for dynamic scenes it is still difficult to compute at interactive rates. Recent attempts to compute AO in screen space for dynamic scenes either have poor performance or suffer from under-sampling problems. We formulate the problem as a 3D volumetric integral, which maps more naturally to graphics hardware. This integral can be solved using line samples to improve the under-sampling problems that plague other techniques. Following the idea of line integrals to its logical conclusion, we show results using area samples that use a simple statistical model of the depth buffer that allows us to use a single sample. We also discuss strategies for generating point, line, and area sample patterns along with ways to incorporate the surface normal into the volume obscurance calculation.
Figure 1: Indirect light computed in reduced subspaces for a cave with 19 blocks and 4 lights. We derive low-dimensional transport operators, on simple proxy shapes, that are warped and combined at run-time, at > 475 FPS on high-end GPUs and > 45 FPS on mobile platforms, and can model indirect light at surfaces (with detailed normal variation) and within volumes of large-scale scene geometry. AbstractMany rendering algorithms willingly sacrifice accuracy, favoring plausible shading with high-performance. Modular Radiance Transfer (MRT) models coarse-scale, distant indirect lighting effects in scene geometry that scales from high-end GPUs to low-end mobile platforms. MRT eliminates scene-dependent precomputation by storing compact transport on simple shapes, akin to bounce cards used in film production. These shapes' modular transport can be instanced, warped and connected on-the-fly to yield approximate light transport in large scenes. We introduce a prior on incident lighting distributions and perform all computations in low-dimensional subspaces. An implicit lighting environment induced from the low-rank approximations is in turn used to model secondary effects, such as volumetric transport variation, higher-order irradiance, and transport through lightfields. MRT is a new approach to precomputed lighting that uses a novel low-dimensional subspace simulation of light transport to uniquely balance the need for high-performance and portable solutions, low memory usage, and fast authoring iteration.
Figure 1: Indirect light computed in reduced subspaces for a cave with 19 blocks and 4 lights. We derive low-dimensional transport operators, on simple proxy shapes, that are warped and combined at run-time, at > 475 FPS on high-end GPUs and > 45 FPS on mobile platforms, and can model indirect light at surfaces (with detailed normal variation) and within volumes of large-scale scene geometry. AbstractMany rendering algorithms willingly sacrifice accuracy, favoring plausible shading with high-performance. Modular Radiance Transfer (MRT) models coarse-scale, distant indirect lighting effects in scene geometry that scales from high-end GPUs to low-end mobile platforms. MRT eliminates scene-dependent precomputation by storing compact transport on simple shapes, akin to bounce cards used in film production. These shapes' modular transport can be instanced, warped and connected on-the-fly to yield approximate light transport in large scenes. We introduce a prior on incident lighting distributions and perform all computations in low-dimensional subspaces. An implicit lighting environment induced from the low-rank approximations is in turn used to model secondary effects, such as volumetric transport variation, higher-order irradiance, and transport through lightfields. MRT is a new approach to precomputed lighting that uses a novel low-dimensional subspace simulation of light transport to uniquely balance the need for high-performance and portable solutions, low memory usage, and fast authoring iteration.
No abstract
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.