TY  - CONF
AU  - Rolff, Tim
AU  - Schmidt, Susanne
AU  - Li, Ke
AU  - Steinicke, Frank
AU  - Frintrop, Simone
TI  - VRS-NeRF: Accelerating Neural Radiance Field Rendering with Variable Rate Shading
PB  - IEEE
M1  - PUBDB-2023-07544
SP  - 243 - 252
PY  - 2023
AB  - Recent advancements in Neural Radiance Fields (NeRF) provide enormous potential for a wide range of Mixed Reality (MR) applications. However, the applicability of NeRF to real-time MR systems is still largely limited by the rendering performance of NeRF. In this paper, we present a novel approach for Variable Rate Shading for Neural Radiance Fields (VRS-NeRF). In contrast to previous techniques, our approach does not require training multiple neural networks or re-training of already existing ones, but instead utilizes the raytracing properties of NeRF. This is achieved by merging rays depending on a variable shading rate, which reduces the overall number of queries to the neural network. We demonstrate the generalizability of our approach by implementing three alternative functions for the determination of the shading rate. The first method uses the gaze of users to effectively implement a foveated rendering technique in NeRF. For the other two techniques, we utilize shading rates based on edges and saliency. Based on a psychophysical experiment and multiple image-based metrics, we suggest a set of parameters for each technique, yielding an optimal tradeoff between rendering performance gain and perceived visual quality.
T2  - 2023 IEEE International Symposium on Mixed and Augmented Reality
CY  - 16 Oct 2023 - 20 Oct 2023, Sydney (Australia)
Y2  - 16 Oct 2023 - 20 Oct 2023
M2  - Sydney, Australia
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.1109/ISMAR59233.2023.00039
UR  - https://bib-pubdb1.desy.de/record/599818
ER  -