Binocular Focused Vision: Task-Query-Driven Depth Estimation and Spatial Relation Modeling for Embodied Intelligence

Modern stereo matching achieves high-quality dense metric depth, yet its prevailing formulation remains task-agnostic: all pixels are treated as equally important. In many query-driven applications, however, the location of interest is known in advance, motivating geometry estimation that is explicitly conditioned on where accuracy is needed. We introduce Binocular Focused Vision and propose FocusStereo, a unified framework for query-conditioned stereo geometry and spatial reasoning. Given rectified stereo images, camera calibration, and a spatial query, FocusStereo conditions stereo correspondence formation on the query, reconstructs local matching distributions, decodes bounded metric geometry, selectively integrates reliable updates, and derives query-centered 3D relations. Under a unified query-centric protocol on VKITTI2 and HAMMER, FocusStereo reduces Focus AbsRel by 65.17% and 27.04% and Query-XYZ error by 61.13% and 15.37%, respectively, relative to the strongest competing result for each metric. Controlled experiments with identical initialization further isolate these gains to query-conditioned reasoning, with only minor changes in global error. These results show that spatial queries can effectively condition stereo correspondence, extending stereo perception from task-agnostic dense reconstruction to query-conditioned geometry and spatial reasoning.