VoxAnchor: Explicit Voxel-Semantic Grounding for Spatial Understanding in Videos

Although Multimodal Large Language Models (MLLMs) havemade impressive progress in understanding videos—such as recognizingobjects, actions, and events—they still struggle with spatial reasoning.In particular, they have difficulty forming a consistent 3D understandingof a scene from separate 2D video frames. Unlike humans, who can natu-rally infer depth, distance, object size, and their own movement throughspace, these models often lack the ability to accurately reconstruct a co-herent 3D environment. This limitation makes it challenging for them toestimate real-world measurements, such as how far an object is or howmuch the camera has moved, when relying only on 2D image sequences.To bridge these gaps, we propose VoxAnchor, a framework for explicitspatial-semantic grounding. Specifically, we reify the 2D video sequenceinto a spatiotemporally continuous representation by unprojecting visualtokens, geometric features, and view-consistent semantics into a unified3D voxel grid. To facilitate precise spatial reasoning, we propose a Voxel-Aware Attention mechanism, which constrains feature interactions withinphysically consistent volumetric units. This approach enables the modelto effectively aggregate fragmented observations and infer metric-awarespatial relationships from monocular video. VoxAnchor sets a new state-of-the-art on VSI (66.8%) and VSTI (66.3%) benchmarks, outperform-ing significantly more parameter-intensive models and confirming thatexplicit geometric grounding effectively bridges the 2D-to-3D gap. Codewill be available at https://github.com/Embrace-Arch/VoxAnchor.