Zhaoxi Zhang, Hao Liang, Linxuan Wu, Yiming Wu, Yifu Yuan, Kun Liang, Chenming Tang, Meiyi Qiang, Wentao Zhang, Weikang Li · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26659 · cited by 0 works (OpenAlex) · open-access PDF
然而,空间问答提供了有限的证据,表明模型是否可以通过自主探索构建准确的计量图。我们引入了SpatialScout,一个基准来评估视觉语言模型(VLM)的观察在一个活跃的对象级3D地图上。SpatialScout包含80个包含3140个目标对象的完整室内场景。通过对象库存,RGB观测,相机校准和相机姿势,我们引入了SpatialScout,一个基准来评估视觉语言模型(VLM)的对象中心。该任务需要协调视点选择,交叉视觉联系,地理测量估计,以及在边界互动预算下进行地图审查。评估共计测量了对象的定位
Machine translation (Meta M2M100); the English original follows.
Spatial intelligence requires agents to actively acquire visual evidence and integrate observations into a coherent representation of the three-dimensional world. Yet spatial question answering offers limited evidence of whether models can construct accurate metric maps through autonomous exploration. We introduce SpatialScout, a benchmark for evaluating vision-language model (VLM) agents on active object-level 3D mapping. SpatialScout comprises 80 diverse indoor scenes containing 3,140 target objects. Given an object inventory, RGB observations, camera calibration, and camera poses, agents control a camera to explore each scene and estimate object centers in a shared coordinate frame. The task requires coordinating viewpoint selection, cross-view association, geometric estimation, and map revision under bounded interaction budgets. Evaluation jointly measures localization accuracy and target coverage through object-scale overlap scores that penalize missing predictions. Experiments with frontier VLMs reveal substantial limitations: the strongest evaluated model, Claude Opus 5.5, achieves a mean overlap score of 41.71%, with 47.23% of targets exceeding an intersection-over-union (IoU) threshold of 0.5 and only 3.25% exceeding 0.9. These results reveal a substantial gap between recovering coarse object layouts and constructing complete, precise metric maps. SpatialScout provides a quantitative testbed for developing agents that build reliable 3D spatial representations through active perception. Code is available at https://github.com/JesseZZZZZ/SpatialScout.
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
Topics: Artificial intelligence, Computer science, Object (grammar), Testbed, Representation (politics), Computer vision
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.26659, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange