Abstract

Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline. This paper designs a two-way coupling between mesh and NeRF during rendering and simulation. We first review the light transport equations for both mesh and NeRF, then distill them into an efficient algorithm for updating radiance and throughput along a cast ray with an arbitrary number of bounces. To resolve the discrepancy between the linear color space that the path tracer assumes and the sRGB color space that standard NeRF uses, we train NeRF with High Dynamic Range (HDR) images. We also present a strategy to estimate light sources and cast shadows on the NeRF. Finally, we consider how the hybrid surface-volumetric formulation can be efficiently integrated with a high-performance physics simulator that supports cloth, rigid and soft bodies. The full rendering and simulation system can be run on a GPU at interactive rates. We show that a hybrid system approach outperforms alternatives in visual realism for mesh insertion, because it allows realistic light transport from volumetric NeRF media onto surfaces, which affects the appearance of reflective/refractive surfaces and illumination of diffuse surfaces informed by the dynamic scene.

Keywords:
Computer science Radiance Rendering (computer graphics) High dynamic range Computer graphics (images) Computer vision Artificial intelligence Ray casting Visualization Volume rendering Optics Physics Dynamic range

Metrics

19
Cited By
12.58
FWCI (Field Weighted Citation Impact)
92
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Computer Graphics and Visualization Techniques
Physical Sciences →  Computer Science →  Computer Graphics and Computer-Aided Design
3D Shape Modeling and Analysis
Physical Sciences →  Engineering →  Computational Mechanics
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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