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 input densities, and specific hardware constraints? Answering this overarching question requires a holistic assessment, yet current models are mainly evaluated on specific domains for which they were specifically designed or trained. Such evaluations are intrinsically limited by narrow paradigm coverage, limited scene domains, and arbitrary frame sampling, making it fundamentally difficult to assess their true generalization capabilities. To address this gap, we present SpatialBench, a cross-paradigm, domain-diverse benchmark for spatial foundation models with deterministic sampling. SpatialBench features unprecedented scale and rigorous deterministic design, comprising 19 datasets and 546 scenes across 5 diverse spatial domains. It comprehensively evaluates 41 models across 6 paradigms on 5 task suites under 4 different input density settings. Our extensive evaluation reveals that current models are not yet all-round players, and uncovers crucial insights for future advancement. Specifically, we demonstrate that full-context attention maximizes accuracy while bounded-memory strategies unlock long-sequence scalability. Moreover, our empirical evaluations in challenging embodied and egocentric tasks demonstrate that strict domain alignment and high data quality are far more critical to performance than simple dataset scaling. Furthermore, to address the largest data gap identified in our analysis, we go beyond evaluation by introducing a large-scale dataset, DA-Next-5M, and a strong baseline model, DA-Next, pushing the boundaries of spatial representation learning.

翻译：尽管空间基础模型在标准数据集上展现了令人瞩目的性能，但一个关键问题依然存在：它们是否真正是能够稳健泛化于多样化下游任务、任意视角、动态场景域、可变输入密度及特定硬件约束的全能型选手？回答这一根本性问题需要整体评估，然而当前模型主要针对其专门设计或训练的特定领域进行评估。此类评估本质受限于狭窄的范式覆盖、有限的场景域及任意帧采样，使得评估其真实泛化能力面临根本性困难。为填补这一空白，我们提出SpatialBench——一个面向空间基础模型的跨范式、多领域基准测试，采用确定性采样策略。SpatialBench具备空前的规模和严谨的确定性设计，涵盖5个多样化空间领域的19个数据集和546个场景，系统评估了6种范式下41个模型在4种输入密度设定下的5类任务套件表现。广泛评估表明，当前模型尚未达到全能型选手水平，并揭示了对未来发展的关键洞见。具体而言，我们证明全上下文注意力机制可最大化准确率，而有限记忆策略可解锁长序列可扩展性。此外，在具身化和自我中心等挑战性任务中的实证显示，严格领域对齐与高数据质量对性能的影响远大于简单数据集规模扩展。最后，针对分析中识别的最大数据缺口，我们超越单纯评估，引入大规模数据集DA-Next-5M及强基线模型DA-Next，进一步拓展了空间表征学习的边界。