引入了一个名为SpatialBench的新基准测试，用于评估空间基础模型在不同领域和任务上的泛化能力。该基准测试包含19个数据集和546个场景，揭示了当前模型在处理多样化场景方面尚未精通。该研究还引入了大规模数据集DA-Next-5M和强大的基线模型DA-Next，以推进空间表示学习。 AI
影响 该基准测试将帮助研究人员识别空间基础模型的弱点，并指导未来开发更强大、更通用的系统。
排序理由 该集群描述了一篇关于评估空间基础模型的新学术论文的发布，该论文详细介绍了一个基准测试以及相关数据集/模型。
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SpatialBench presents a comprehensive benchmark for evaluating spatial foundation models across diverse domains and tasks, revealing limitations in current models and introducing DA-Next-5M and DA-Next to advance spatial representation learning.
arXiv:2605.27367v1 Announce Type: new Abstract: While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing robustly across diverse downstream tasks, arbitrary…
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing robustly across diverse downstream tasks, arbitrary viewpoints, shifting scene domains, varying inp…