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Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting

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arxiv 2410.07266 v5 pith:IFDE5CLF submitted 2024-10-09 cs.CV

Weixing Zhang , Zongrui Li , De Ma , Huajin Tang , Xudong Jiang , Qian Zheng , Gang Pan This is my paper · ORCID
classification cs.CV
keywords spikinggaussiansgaussianlopslow-opacityneuronsreconstructionsplatting
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A shared learnable solidness factor turns Gaussian splatting kernels into near-opaque surfels, reducing multi-view depth inconsistency and giving state-of-the-art sparse-view surface reconstruction.

  2. GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction

    cs.CV 2024-11 conditional novelty 6.0 of 10

    GSurf learns a signed distance field supervised by Gaussian splat centers and renders via splatting, yielding compact meshes faster than previous Gaussian-SDF hybrids.

  3. Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A spiking neuron learns the Marching Cubes density threshold inside Nerfacto, and a round-robin schedule stabilizes training to sharpen extracted geometry.

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