AI RESEARCH
VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations
arXiv CS.CV
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ArXi:2603.16506v1 Announce Type: new Multi-view visual reasoning is essential for intelligent systems that must understand complex environments from sparse and discrete viewpoints, yet existing research has largely focused on single-image or temporally dense video settings. In real-world scenarios, reasoning across views requires integrating partial observations without explicit guidance, while collecting large-scale multi-view data with accurate geometric and semantic annotations remains challenging.