Spike cameras generate binary spikes in response to light intensity changes, enabling high-speed visual perception with unprecedented temporal resolution. However, the unique characteristics of spike stream present significant challenges for reconstructing dense 3D scene representations, particularly in dynamic environments and under non-ideal lighting conditions. In this paper, we introduce DSNeRF, the first method to derive a NeRF-based volumetric scene representation from spike camera data. Our approach leverages NeRF's multi-view consistency to establish robust self-supervision, effectively eliminating erroneous measurements and uncovering coherent structures within exceedingly noisy input amidst diverse real-world illumination scenarios. We propose a novel mapping from pixel rays to the spike domain, integrating the spike generation process directly into NeRF training. Specifically, DSNeRF introduces an integrate-and-fire neuron layer that models non-idealities to capture intrinsic camera noise, including both random and fixed-pattern spike noise, thereby enhancing scene fidelity. Additionally, we propose a motion-guided spiking neuron layer and a long-term rendering photometric loss to better align dynamic spike streams, ensuring accurate scene geometry. Our method optimizes neural radiance fields to render photorealistic novel views from continuous spike streams, demonstrating advantages over other vision sensors in certain scenes. Empirical evaluations on both real and simulated sequences validate the effectiveness of our approach. The dataset and source code will be released at https://github.com/BIT-Vision/DSNeRF.
Zhu et al. (Thu,) studied this question.