SpikingPoint: Rethinking Point as Spike for Efficient 3D Point Cloud Analysis

Abstract

Spiking Neural Networks (SNNs), due to their unique spike-based inference mechanism, offer low power consumption and biological plausibility. As a fundamental technology for various real-world applications, 3D point cloud analysis faces significant challenges related to high computational overhead and energy-intensive. In fact, each point can be viewed as a specialized spike data containing positional information in 3D space. Therefore, utilizing the spiking features of SNNs to represent point clouds holds significant potential. However, this exploration faces two main challenges: SNN-compatible architecture design and spike-based 3D spatial modeling. In this work, we introduce the SpikingPoint, a pure Spiking Multi-layer Perceptron (MLP) Architecture that leverages the low power consumption of SNNs and the computational efficiency of linear layers. Furthermore, we propose the spiking 3D Position Embedding (SPE) that effectively models the 3D spatial feature into spike-form features. SpikingPoint achieves competitive performance with a small number of parameters while reducing energy consumption. For example, when achieving similar accuracy, the theoretical energy consumption of our method is reduced by 97.1% compared to the mainstream ANN-based KPConv. With fewer parameters, SpikingPoint surpasses the state-of-the-art SNN-based P2SResLNet on ModelNet40 by 1.42% in accuracy.