Spiking neural networks (SNNs) have been thriving on numerous tasks to leverage their promising energy efficiency and exploit their potentialities as biologically plausible intelligence. Meanwhile, the Neural Radiance Fields (NeRF) render high-quality 3D scenes with massive energy consumption, but few works delve into the energy-saving solution with a bio-inspired approach. In this paper, we propose SpikingNeRF, which aligns the radiance ray with the temporal dimension of SNN, to naturally accommodate the SNN to the reconstruction of Radiance Fields. Thus, the computation turns into a spike-based, multiplication-free manner, reducing the energy consumption. In SpikingNeRF, each sampled point on the ray is matched onto a particular time step, and represented in a hybrid manner where the voxel grids are maintained as well. Based on the voxel grids, sampled points are determined whether to be masked for better training and inference. However, this operation also incurs irregular temporal length. We propose the temporal padding strategy to tackle the masked samples to maintain regular temporal length, i.e., regular tensors, and the temporal condensing strategy to form a denser data structure for hardware-friendly computation. Extensive experiments on various datasets demonstrate that our method reduces the 70.79\% energy consumption on average and obtains comparable synthesis quality with the ANN baseline.

翻译：脉冲神经网络（SNN）凭借其优越的能效优势以及作为生物合理性智能的潜力，已在众多任务中蓬勃发展。与此同时，神经辐射场（NeRF）虽能渲染出高质量的三维场景，但能耗巨大，而鲜有研究探索基于生物启发方法的节能方案。本文提出SpikingNeRF，将辐射光线与SNN的时间维度对齐，使SNN自然适配辐射场的重建任务。由此，计算转变为基于脉冲、无需乘法的模式，降低了能耗。在SpikingNeRF中，光线上的每个采样点被匹配至特定时间步，并以混合方式表示，同时保留体素网格。基于体素网格，可判定采样点是否被掩码以优化训练与推理。然而，该操作会导致不规则的时间长度。我们提出时间填充策略处理掩码样本以维持规则的时间长度（即规则张量），以及时间凝聚策略形成更密集的数据结构以实现硬件友好计算。在多个数据集上的大量实验表明，本方法平均降低70.79%的能耗，且合成质量与ANN基线方法相当。