RNA: Relightable Neural Assets

IF 7.8 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING ACM Transactions on Graphics Pub Date : 2024-09-13 DOI:10.1145/3695866
Krishna Mullia, Fujun Luan, Xin Sun, Miloš Hašan
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Abstract

High-fidelity 3D assets with materials composed of fibers (including hair), complex layered material shaders, or fine scattering geometry are critical in high-end realistic rendering applications. Rendering such models is computationally expensive due to heavy shaders and long scattering paths. Moreover, implementing the shading and scattering models is non-trivial and has to be done not only in the 3D content authoring software (which is necessarily complex), but also in all downstream rendering solutions. For example, web and mobile viewers for complex 3D assets are desirable, but frequently cannot support the full shading complexity allowed by the authoring application. Our goal is to design a neural representation for 3D assets with complex shading that supports full relightability and full integration into existing renderers. We provide an end-to-end shading solution at the first intersection of a ray with the underlying geometry. All shading and scattering is precomputed and included in the neural asset; no multiple scattering paths need to be traced, and no complex shading models need to be implemented to render our assets, beyond a single neural architecture. We combine an MLP decoder with a feature grid. Shading consists of querying a feature vector, followed by an MLP evaluation producing the final reflectance value. Our method provides high-fidelity shading, close to the ground-truth Monte Carlo estimate even at close-up views. We believe our neural assets could be used in practical renderers, providing significant speed-ups and simplifying renderer implementations.
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RNA:可重燃的神经资产
在高端逼真渲染应用中,具有由纤维(包括头发)、复杂分层材质着色器或精细散射几何体组成的材质的高保真 3D 资产至关重要。由于着色器和散射路径较长,渲染这类模型的计算成本很高。此外,实现着色和散射模型并非易事,不仅需要在三维内容制作软件中完成(这必然很复杂),还需要在所有下游渲染解决方案中完成。例如,复杂三维资产的网络和移动浏览器固然可取,但往往无法支持创作应用程序所允许的全部着色复杂性。我们的目标是为具有复杂着色的三维资产设计一种神经表示法,它支持完全的可重照性,并能完全集成到现有的渲染器中。我们在光线与底层几何体的第一个交叉点提供端到端的着色解决方案。所有的着色和散射都是预先计算好的,并包含在神经资产中;除了单一的神经架构外,无需追踪多个散射路径,也无需实施复杂的着色模型来渲染我们的资产。我们将 MLP 解码器与特征网格相结合。着色包括查询特征向量,然后通过 MLP 评估得出最终反射值。我们的方法能提供高保真的阴影效果,即使在近距离观察时也能接近地面实况蒙特卡洛估计值。我们相信,我们的神经资产可用于实际的渲染器中,显著提高速度并简化渲染器的实现。
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来源期刊
ACM Transactions on Graphics
ACM Transactions on Graphics 工程技术-计算机:软件工程
CiteScore
14.30
自引率
25.80%
发文量
193
审稿时长
12 months
期刊介绍: ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.
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PhysFiT: Physical-aware 3D Shape Understanding for Finishing Incomplete Assembly Synchronized tracing of primitive-based implicit volumes TriHuman : A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis DAMO: A Deep Solver for Arbitrary Marker Configuration in Optical Motion Capture RNA: Relightable Neural Assets
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