Spiking DGCNN: Max-First Graph Spiking Networks for Efficient Point Cloud Learning

Spiking neural networks (SNNs) offer sparse, event-driven computation for energy-efficient learning, but the naive spiking conversion of graph-based point cloud networks remains poorly understood. EdgeConv relies on neighbourhood-wise max aggregation to select discriminative local responses. Simply replacing LeakyReLU with spiking neurons in EdgeConv quantizes features before aggregation, weakening credit assignment and causing severe performance degradation. We therefore propose Spiking DGCNN, built on a Max-First Spiking Rule that performs neighbourhood selection on continuous-valued features before spike generation. We further introduce an Adaptive Pseudo-Temporal Expansion-Compression (APTEC) module that supports lightweight pseudo-temporal evidence accumulation through leaky spiking integration, adaptive thresholding, and binary spike compression. Ablation experiments show that Max-First Spiking Rule improves Spike-before-Max baseline from 79.09% to 91.77% at T=1 and from 84.11% to 92.18% at T=4 on ModelNet40. The complete model with APTEC achieves 92.38% accuracy, closely matching the 92.9% of DGCNN, while reducing the operation-level energy estimate from 11.23 mJ to 0.22 mJ.