SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts

Recent Multimodal Large Language Models (MLLMs) strug-gle to bridge the representational gap between 2D semantic understand-ing and 3D spatial geometry. Existing 3D-aware models either rely oncostly 3D-specific data or utilize RGB-only inputs with heuristic sam-pling and monolithic, shallow fusion, which respectively disrupt essentialspatiotemporal connectivity and induce modality contention across di-verse spatial tasks. To overcome these bottlenecks, we introduce SpaR3D-MoE, an end-to-end framework that enables adaptive spatial reasoningby equipping MLLMs with geometry-aware capabilities from only sparseRGB inputs. First, we propose an adaptive spatiotemporal manifoldsampling mechanism that constructs a geometry-aware spatiotemporalgraph to extract informative keyframes, effectively mitigating sequenceredundancy while preserving the scene’s topological connectivity. Second,we introduce the heterogeneous geometry-inductive Mixture-of-Expertsdriven by an instruction-pose aware router, which adaptively routes mul-timodal tokens to specialized experts, resolving the cross-modal con-tention inherent in monolithic fusion. Extensive experiments on VSI-Bench, ScanQA, and SQA3D demonstrate that our method achievesstate-of-the-art performance. Notably, SpaR3D-MoE achieves the highestaverage score of 63.5 on VSI-Bench, outperforming the strongest base-line by 7.8 absolute points, alongside relative improvements of 35.4% and51.4% in Route Plan and Relative Direction tasks, respectively.