💡 The paper introduces SpatialBench, a comprehensive benchmark for evaluating spatial foundation models across various domains and tasks. The goal is to assess whether these models can generalize robustly across different tasks, viewpoints, scene domains, input densities, and hardware constraints. Current models are mainly evaluated on specific domains they were designed for, which limits their assessment. SpatialBench addresses this gap by featuring 19 datasets and 546 scenes across 5 diverse spatial domains, evaluating 41 models across 6 paradigms on 5 task suites under 4 different input density settings.

The evaluation reveals that current models are not all-round players, and the authors identify key insights for future advancement. They find that full-context attention maximizes accuracy, while bounded-memory strategies enable long-sequence scalability. The authors also demonstrate that domain alignment and data quality are more critical to performance than dataset size. To address the largest data gap, they introduce a large-scale dataset, DA-Next-5M, and a strong baseline model, DA-Next, to advance spatial representation learning.

The paper's contributions include a holistic assessment of spatial foundation models, a comprehensive benchmark with unprecedented scale and rigorous design, and the introduction of a new dataset and model to push the boundaries of spatial representation learning. The results provide valuable insights for the development of more robust and generalizable spatial foundation models. Overall, the paper highlights the limitations of current models and provides a foundation for future research to create more all-round players in the field of spatial representation learning.