Seek-and-View Reasoning for Multi-View Spatial Understanding

Key points

• Existing approaches to multi-view spatial reasoning operate largely on sparse input views.1
• Vision-language models (VLMs) are thus restricted to understand a scene and infer spatial relations within these fixed views, leading to fragile cross-view alignment and geometry-to-language bottleneck.1
• To address these issues, we formulate a novel Seek-and-View reasoning approach to find implicit cross-view spatial evidence by locating a question-relevant view to support the spatial reasoning.1
• Overall, by revealing spatial evidence through view-grounded reasoning, Vantage can largely reduce reliance on language-based cross-view alignment and improve multi-view spatial understanding.1

Sources (1)

• [1]Seek-and-View Reasoning for Multi-View Spatial Understanding
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 12:18 PM

  To realize this approach, we propose Vantage, a training-free model-agnostic reasoning framework that pairs a VLM with a 3D foundation model: a viewpoint-grounded reasoning stage for question analysis and view planning, followed by a geometry-grounded evidence augmentation stage to effectively synthesize and incorporate visual evidence into the final reasoning.

  Existing approaches to multi-view spatial reasoning operate largely on sparse input views.

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