Mamba-3VL: Taming State Space Model for 3D Vision Language Learning

multi-modal features. In this paper, we propose Mamba-3VL, a pioneering 3D-VL framework to model complex intra-and inter-modality correlations and enhance spatial relation reasoning, while guaranteeing top-tier performance, high efficiency, and generalization potential for 3D-VL tasks. Specifically, Mamba Mixer explicitly models 3D-VL interaction via channel twisting and relation-prioritized spatial scanning policy. It maximally retain spatial relation of objectcentric features. To further provide precise spatial encoding for mamba, we develop Instance-aware Dynamic Position Adapter (IDPA) to dynamically adjust instance-specific positional embeddings and enhance local spatial relation of 3D objects. Extensive results validate Mamba-3VL trumps other competitors on seven 3D-VL benchmarks and showcases versatile potentials for challenging Embodied AI tasks.