PointMamba++: Rethinking Ordering and Convolution Strategy of State Space Model for Point Cloud Analysis

ABSTRACT With the linear complexity and long‐sequence global modelling capability, Mamba becomes a competitor to Transformer architectures in point clouds analysis. However, the designs of traditional 1D convolutions and reordering strategies, do not match the inherently unordered nature of point clouds, which constrain the performance enhancement. In this work, we rethink the ordering and convolution strategy of the PointMamba, and present a novel architecture named PointMamba++ to more effectively aggregate local structural features and achieve a superior accuracy and computation trade‐off. Specifically, we design a point‐edge convolution to aggregate neighbourhood features of point cloud tokens, which replaces 1D convolution layers in traditional Mamba modules and does not perform convolution by sequence but according to geometric relationships. Furthermore, considering that forcibly ordering point clouds is not conducive to learning local geometric features and easily leads to unstable sequence dependencies, we design a sequence‐independent BiMamba module, which adopts two reverse and parallel scanning paths, to reduce the dependency on sequential scanning of Mamba while enhancing point cloud representation abilities. Extensive experiments show that PointMamba++ surpasses typical convolution‐based and Transformer‐based architectures, and achieves state‐of‐the‐art performance on multiple tasks including shape classification, part segmentation, and semantic segmentation.