用脉冲神经元优化3D高斯溅射，提升重建精度并降低成本
Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting
- 将脉冲神经元融入高斯溅射流程，分别控制不透明度与表示函数
- 降低低不透明度部分占比，使表面重建更准确且存储训练成本更低
- 适合关注高效3D重建与神经渲染的开发者与研究者
3D高斯溅射能在数分钟内完成3D场景重建。尽管近期在提升表面重建精度方面取得进展，现有方法仍存在偏差，且存储和训练效率低下。本文指出效率问题与重建偏差的根源在于生成高斯分布中低不透明度部分（LOPs）的整合。我们发现LOPs包含整体低不透明度的高斯（LOGs）以及高斯的低不透明度尾部（LOTs）。为此提出Spiking GS，通过在高斯溅射流水线中引入脉冲神经元以减少这两类成分。具体地，在扁平化3D高斯的不透明度与表示函数中分别引入全局和局部全精度积分-发放脉冲神经元。此外，结合脉冲神经元阈值与新的高斯尺度判据，改进密度控制策略。实验表明，该方法可在更低成本下实现更精确的表面重建。补充材料与代码见https://github.com/zju-bmi-lab/SpikingGS。
原文摘要 · Abstract (English)
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 a new criterion on the scale of Gaussians. Our method can represent more accurate reconstructed surfaces at a lower cost. The supplementary material and code are available at https://github.com/zju-bmi-lab/SpikingGS.
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