A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are continuous functions, however, real geometry or density field is frequently discontinuous at the interface between the air and the surface. Such a contrary brings the problem of unfaithful geometry representation. To this end, this paper proposes spiking NeRF, which leverages spiking neuron and a hybrid Artificial Neural Network (ANN)-Spiking Neural Network (SNN) framework to build a discontinuous density field for faithful geometry representation. Specifically, we first demonstrate the reason why continuous density fields will bring inaccuracy. Then, we propose to use the spiking neurons to build a discontinuous density field. We conduct comprehensive analysis for the problem of existing spiking neuron models and then provide the numerical relationship between the parameter of spiking neuron and the theoretical accuracy of geometry, Based on this, we propose a bounded spiking neuron to build the discontinuous density field. Our results achieve SOTA performance. Our code and data will be released to the public.

翻译：现有基于NeRF的方法成功的关键在于通过多层感知器（MLP）构建神经密度场来表示几何结构。然而，MLP是连续函数，而真实几何或密度场在空气与表面界面处往往是不连续的。这种矛盾导致了几何表示不忠实的问题。为此，本文提出脉冲神经辐射场（Spiking NeRF），利用脉冲神经元和混合人工神经网络（ANN）-脉冲神经网络（SNN）框架构建不连续密度场，以实现忠实的几何表示。具体而言，我们首先论证了连续密度场导致不精确性的原因，随后提出利用脉冲神经元构建不连续密度场。针对现有脉冲神经元模型存在的问题，我们进行了全面分析，并推导出脉冲神经元参数与几何理论精度之间的数值关系。基于此，我们提出一种有界脉冲神经元来构建不连续密度场。实验结果达到当前最优性能。代码与数据将公开发布。