Map2Thought: Explicit 3D Spatial Reasoning via Metric Cognitive Maps

TL;DR

Map2Thought introduces an interpretable 3D spatial reasoning framework for vision-language models, combining metric-based spatial representations with explicit geometric reasoning to improve explainability and performance with less supervision.

Contribution

The paper presents a novel framework integrating Metric Cognitive Maps and Chain-of-Thought reasoning for explicit 3D spatial understanding in vision-language models.

Findings

Achieves 59.9% accuracy with half supervision, close to full supervision baseline.

Outperforms state-of-the-art methods by over 4% on VSI-Bench.

Enables explainable 3D reasoning with improved accuracy across training data subsets.

Abstract

We propose Map2Thought, a framework that enables explicit and interpretable spatial reasoning for 3D VLMs. The framework is grounded in two key components: Metric Cognitive Map (Metric-CogMap) and Cognitive Chain-of-Thought (Cog-CoT). Metric-CogMap provides a unified spatial representation by integrating a discrete grid for relational reasoning with a continuous, metric-scale representation for precise geometric understanding. Building upon the Metric-CogMap, Cog-CoT performs explicit geometric reasoning through deterministic operations, including vector operations, bounding-box distances, and occlusion-aware appearance order cues, producing interpretable inference traces grounded in 3D structure. Experimental results show that Map2Thought enables explainable 3D understanding, achieving 59.9% accuracy using only half the supervision, closely matching the 60.9% baseline trained with the full dataset. It consistently outperforms state-of-the-art methods by 5.3%, 4.8%, and 4.0% under 10%, 25%, and 50% training subsets, respectively, on the VSI-Bench.