Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting
3D Gaussian Splatting is capable of reconstructing 3D scenes in minutes. Despite recent advances in improving surface reconstruction accuracy, the reconstructed results still exhibit bias and suffer from inefficiency in storage and training. This paper provides a different observation on the cause of the inefficiency and the reconstruction bias, which is attributed to the integration of the low-opacity parts (LOPs) of the generated Gaussians. We show that LOPs consist of Gaussians with overall low-opacity (LOGs) and the low-opacity tails (LOTs) of Gaussians. We propose Spiking GS to reduce such two types of LOPs by integrating spiking neurons into the Gaussian Splatting pipeline. Specifically, we introduce global and local full-precision integrate-and-fire spiking neurons to the opacity and representation function of flattened 3D Gaussians, respectively. Furthermore, we enhance the density control strategy with spiking neurons' thresholds and an new criterion on the scale of Gaussians. Our method can represent more accurate reconstructed surfaces at a lower cost.
3D高斯点技术能够在几分钟内重建3D场景。尽管在提高表面重建精度方面取得了进展，但重建结果仍然存在偏差，并且在存储和训练效率方面表现不佳。本文提供了对这些效率问题和重建偏差的不同观察，指出问题源于生成的高斯点中的低不透明部分（LOPs）的整合。我们展示了LOPs包括整体低不透明高斯点（LOGs）和高斯点的低不透明尾部（LOTs）。为此，我们提出了Spiking GS，通过将尖峰神经元集成到高斯点管道中来减少这两类LOPs。具体而言，我们在展平的3D高斯点的不透明度和表示函数中，分别引入了全精度整合-触发（integrate-and-fire）尖峰神经元，用于全局和局部控制。此外，我们通过尖峰神经元的阈值和一种新的高斯点尺度标准，增强了密度控制策略。我们的方法可以在更低的成本下表示更精确的重建表面。