FPO++: efficient encoding and rendering of dynamic neural radiance fields by analyzing and enhancing Fourier PlenOctrees

Published: 01 Jan 2024, Last Modified: 12 Aug 2024Vis. Comput. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic neural radiance fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we present a novel density encoding that adapts the Fourier-based compression to the characteristics of the transfer function used by the underlying volume rendering procedure and leads to a substantial reduction of artifacts in the dynamic model. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes.
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