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. Furthermore, we show an augmentation of the training data that relaxes the periodicity assumption of the compression. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes.
Citation:
S. Rabich, P. Stotko, and R. Klein, “FPO++: Efficient encoding and rendering of dynamic neural radiance fields by analyzing and enhancing Fourier PlenOctrees,” arXiv (Cornell University), Jan. 2023, doi: 10.48550/arxiv.2310.20710.
More Information:
Open source: https://doi.org/10.48550/arXiv.2310.20710