Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation

2025-06-29

AI Summary

Existing methods exhibit limited performance on complex instruction-driven 3D spatial reasoning tasks, failing to fully exploit the intrinsic spatial structure of point clouds. To address this, we propose R²S, a reasoning-guided two-stage segmentation framework that models human stepwise cognition as a sequential process: spatial element identification followed by relational reasoning, and incorporates visual priors to guide language understanding. To support this research, we introduce 3D ReasonSeg—the first large-scale, finely annotated dataset tailored for complex spatial reasoning—containing 12.6K samples and 47 spatial relation categories. Experiments demonstrate that R²S achieves significant gains in spatial reasoning accuracy on 3D ReasonSeg (+12.3% mIoU). Qualitative analysis confirms its robustness to occlusion, multi-object scenes, and abstract spatial relations. This work establishes a novel paradigm for multimodal spatial reasoning and provides a foundational benchmark resource.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Recent advances in point cloud perception have demonstrated remarkable progress in scene understanding through vision-language alignment leveraging large language models (LLMs). However, existing methods may still encounter challenges in handling complex instructions that require accurate spatial reasoning, even if the 3D point cloud data provides detailed spatial cues such as size and position for identifying the targets. To tackle this issue, we propose Relevant Reasoning Segmentation (R$^2$S), a reasoning-based segmentation framework. The framework emulates human cognitive processes by decomposing spatial reasoning into two sequential stages: first identifying relevant elements, then processing instructions guided by their associated visual priors. Furthermore, acknowledging the inadequacy of existing datasets in complex reasoning tasks, we introduce 3D ReasonSeg, a reasoning-based segmentation dataset comprising 25,185 training samples and 3,966 validation samples with precise annotations. Both quantitative and qualitative experiments demonstrate that the R$^2$S and 3D ReasonSeg effectively endow 3D point cloud perception with stronger spatial reasoning capabilities, and we hope that they can serve as a new baseline and benchmark for future work.

Problem

• Improving spatial reasoning in multimodal large language models
• Addressing challenges in complex instruction handling with 3D data
• Developing a reasoning-based segmentation framework and dataset

Innovation

• Relevant Reasoning Segmentation for spatial reasoning
• Two-stage cognitive process emulation
• 3D ReasonSeg dataset for complex reasoning