Enhancing Discontinuous Radiance Fields with Continuous representation

Authors: Yi Gu, Jiahang Cao, Jiaxu Wang, Renjing Xu, Zhaorui Wang, Dongjun YE
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: neural rendering, neural geometry learning, spiking neural network

Abstract:
Most NeRF-based methods utilize continuous neural implicit functions (e.g., MLPs) to represent the underlying geometry, which tends to be discontinuous at the interface between the air and the surface. To alleviate this conflict, Spiking NeRF~\cite{liao2024spiking} leverages spiking neurons with a learnable threshold to split the media and object surfaces, achieving promising results but suffering from a lengthy training process. Therefore, in this work, we follow the spirit of Spiking NeRF and extend it to fast grid-based neural fields. Meanwhile, to handle the side effects of spiking neurons, such as holes and floaters, we propose a dual-branch training strategy that optimizes the density field using both continuous and discontinuous density values. The discontinuous branch mirrors Spiking NeRF, with a fixed surrogate gradient, while the continuous branch use full range density values to compute the volume rendering equation, allowing only densities below the threshold to be optimized. Our proposed method can significantly improve both geometry and rendering quality with negligible additional costs. Extensive experimental results show that our method effectively outperforms previous methods on both synthetic and real-world datasets, including solid and transparent objects.

Decision: withdrawn
Reviewer scores: 0

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