Decoupled Spiking Mamba for High-Fidelity Neural Radiance Fields

Neural Radiance Fields (NeRF) built on Artificial Neural Networks (ANNs) dominate 3D reconstruction and rendering but incur a severe energy bottleneck. Spiking Neural Networks (SNNs) replace Multiply-Accumulate (MAC) with sparse Accumulate (AC) operations for low-power 3D representation, yet SNN-based NeRF methods, despite narrowing the metric gap to ANN-based NeRF, still suffer from geometric fragmentation and floater artifacts. Fragmentation arises because NeRF queries a Multi-Layer Perceptron (MLP) independently per point without neighborhood context, and it coincides with unstable spike firing in deep spiking layers; floater artifacts arise from density ambiguity in unconstrained volume rendering, which the limited capacity of SNNs amplifies. To address these issues, this paper proposes DSMamba-NeRF (Decoupled Spiking Mamba for Neural Radiance Fields), an end-to-end high-fidelity spiking architecture. A Decoupled Spiking Mamba (DSMamba) module tackles fragmentation by splitting voxel channels into a global branch that aggregates neighborhood topology via multidirectional state space scanning and a local branch that preserves high-frequency boundaries through 3D spiking convolutions. A Surface-aware spiking Rendering and Refinement (SRF) mechanism replaces unconstrained density with a spiking VolSDF that imposes explicit geometric constraints near the zero level set to suppress floater artifacts. An Adaptive Ternary Spike Neuron (ATSN) with Spike Firing Adaptation (SFA) expands information capacity and modulates the membrane threshold, ensuring stable gradients and an accumulate dominated inference pipeline, with multiplications confined to the compact decoding heads. On synthetic and real-world scenes, DSMamba-NeRF alleviates both defects, attains state-of-the-art rendering metrics among SNN-based NeRF methods, and approaches ANN-based NeRF in visual quality at low energy cost.