MANBO: Mamba-Based Lightweight Attention Network with Priority-Bridged Ordering for Point Cloud Processing

Abstract

Selective state space models (SSMs) enable efficient long-range dependency modeling across language, vision, and 3D domains. However, because SSMs are optimized for sequential processing, they handle spatially irregular inputs such as point clouds poorly. We propose MANBO, which couples a graph-based node priority selection mechanism with the Mamba block to model long-range context while preserving local relational structure. A lightweight graph network learns semantic relations among point patches and derives a priority score for each patch from the concentration of its local attention. A priority–space integration strategy then orders patches by both spatial proximity and the attention-derived priority, and we extend the reconstruction scope of masked pre-training to couple global contextual learning with fine-grained local reconstruction. On ScanObjectNN, ModelNet40, and ShapeNetPart, MANBO achieves 94.76% (OBJ-BG), 93.2%, and 86.3 mIoU, respectively, with consistent improvements over Mamba-based baselines. These results indicate that jointly addressing serialization and representation learning is an effective way to apply SSMs to object-level point cloud understanding.