CLAG-SNN: Compact Local, Abstract Global for Efficient Spiking 3D Point Cloud Recognition

Spiking neural networks (SNNs) offer prominent advantages in efficient 3D point-cloud recognition. However, the discrete nature of spiking representations and the irregular structure of point clouds make it difficult to simultaneously preserve local geometry and form high-level semantic abstractions. Inspired by the hierarchical processing mechanism of biological vision, we propose a **Compact Local, Abstract Global (CLAG)** principle, which preserves nonlinear dependencies before and after compression at the local scale to maintain structure, and suppresses nonlinear dependencies between inputs and spiking features at the global scale to promote abstraction. We instantiate this principle as a hierarchical framework termed CLAG-SNN. First, at the local scale, we propose **Hierarchical Structure-aware Token Compaction (HSTC)**, which preserves local geometric dependencies through structure-aware token aggregation and compaction, achieving local compactness. Furthermore, at the global scale, we propose **Nonlinearity-Aware Abstraction Bottleneck (NAAB)**, which suppresses redundant input–feature nonlinear coupling and abstracts compact tokens into discriminative global semantics. Experiments on multiple 3D point-cloud recognition benchmarks show that CLAG-SNN achieves consistent improvements over the baseline: while improving accuracy, it increases inference speed by up to 24.4% and reduces theoretical energy consumption by up to one-third; with only 1% training data, it still achieves a 3.32% performance gain.