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000599818 0247_ $$2doi$$a10.1109/ISMAR59233.2023.00039
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000599818 037__ $$aPUBDB-2023-07544
000599818 041__ $$aEnglish
000599818 1001_ $$0P:(DE-HGF)0$$aRolff, Tim$$b0$$eCorresponding author
000599818 1112_ $$a2023 IEEE International Symposium on Mixed and Augmented Reality$$cSydney$$d2023-10-16 - 2023-10-20$$gISMAR$$wAustralia
000599818 245__ $$aVRS-NeRF: Accelerating Neural Radiance Field Rendering with Variable Rate Shading
000599818 260__ $$bIEEE$$c2023
000599818 29510 $$a2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) : [Proceedings] - IEEE, 2023. - ISBN 979-8-3503-2838-7 - doi:10.1109/ISMAR59233.2023.00039
000599818 300__ $$a243 - 252
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000599818 520__ $$aRecent advancements in Neural Radiance Fields (NeRF) provide enormous potential for a wide range of Mixed Reality (MR) applications. However, the applicability of NeRF to real-time MR systems is still largely limited by the rendering performance of NeRF. In this paper, we present a novel approach for Variable Rate Shading for Neural Radiance Fields (VRS-NeRF). In contrast to previous techniques, our approach does not require training multiple neural networks or re-training of already existing ones, but instead utilizes the raytracing properties of NeRF. This is achieved by merging rays depending on a variable shading rate, which reduces the overall number of queries to the neural network. We demonstrate the generalizability of our approach by implementing three alternative functions for the determination of the shading rate. The first method uses the gaze of users to effectively implement a foveated rendering technique in NeRF. For the other two techniques, we utilize shading rates based on edges and saliency. Based on a psychophysical experiment and multiple image-based metrics, we suggest a set of parameters for each technique, yielding an optimal tradeoff between rendering performance gain and perceived visual quality.
000599818 536__ $$0G:(DE-HGF)POF4-621$$a621 - Accelerator Research and Development (POF4-621)$$cPOF4-621$$fPOF IV$$x0
000599818 536__ $$0G:(DE-HGF)2019_IVF-HIDSS-0002$$aHIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (2019_IVF-HIDSS-0002)$$c2019_IVF-HIDSS-0002$$x1
000599818 588__ $$aDataset connected to CrossRef Conference
000599818 693__ $$0EXP:(DE-MLZ)NOSPEC-20140101$$5EXP:(DE-MLZ)NOSPEC-20140101$$eNo specific instrument$$x0
000599818 7001_ $$0P:(DE-HGF)0$$aSchmidt, Susanne$$b1
000599818 7001_ $$0P:(DE-H253)PIP1097300$$aLi, Ke$$b2
000599818 7001_ $$0P:(DE-HGF)0$$aSteinicke, Frank$$b3
000599818 7001_ $$0P:(DE-HGF)0$$aFrintrop, Simone$$b4
000599818 773__ $$a10.1109/ISMAR59233.2023.00039
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000599818 9141_ $$y2023
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