ResearchResearch paperRobotics & Embodied AI · Multimodal Models · Computer Vision1 source · Oct 21, 2026

Think-Capture: Active 3D Geometry Acquisition for Spatial Reasoning in Vision-Language Models

We present Think-Capture, a query-conditioned framework that first identifies task-critical objects and then acquires their geometry on demand.

Vision-Language Models (VLMs) excel at 2D recognition but often fail to maintain consistent 3D relations across sparse viewpoints, leading to spatial hallucinations under occlusion and perspective changes.
A 3D toolkit lifts zero-shot masks into a common coordinate frame, and a Symbolic 3D State Abstraction compresses object and camera states into structured text.
Hierarchical Group Relative Policy Optimization (GRPO) trains the VLM on cases in which geometry either helps or misleads, teaching it when to use tool evidence and when to fall back on visual cues.
Think-Capture-4B reaches 54.4% on MindCube-Tiny (+21.5 points over its backbone), 39.1% on the MMSI-Bench positional subset (+7.9), and 42.8% on ViewSpatial (+5.2).

[1]Think-Capture: Active 3D Geometry Acquisition for Spatial Reasoning in Vision-Language Models
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:42 AM
We present Think-Capture, a query-conditioned framework that first identifies task-critical objects and then acquires their geometry on demand.
Vision-Language Models (VLMs) excel at 2D recognition but often fail to maintain consistent 3D relations across sparse viewpoints, leading to spatial hallucinations under occlusion and perspective changes.

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