Spatial Reasoning in Foundation Models: Benchmarking Object-Centric Spatial Understanding

• URL: http://arxiv.org/abs/2509.21922v1
• Date: Fri, 26 Sep 2025 06:06:19 GMT
• Title: Spatial Reasoning in Foundation Models: Benchmarking Object-Centric Spatial Understanding
• Authors: Vahid Mirjalili, Ramin Giahi, Sriram Kollipara, Akshay Kekuda, Kehui Yao, Kai Zhao, Jianpeng Xu, Kaushiki Nag, Sinduja Subramaniam, Topojoy Biswas, Evren Korpeoglu, Kannan Achan,
• Abstract summary: We present a benchmark for object-centric spatial reasoning in foundation models.<n>We find a stable trade-off: detectors such as GroundingDINO and OWLv2 deliver precise boxes with limited relational reasoning.<n>Our study highlights the gap between localization and true spatial understanding, and pointing toward the need for spatially-aware foundation models in the community.
• Score: 8.202861909913791
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Spatial understanding is a critical capability for vision foundation models. While recent advances in large vision models or vision-language models (VLMs) have expanded recognition capabilities, most benchmarks emphasize localization accuracy rather than whether models capture how objects are arranged and related within a scene. This gap is consequential; effective scene understanding requires not only identifying objects, but reasoning about their relative positions, groupings, and depth. In this paper, we present a systematic benchmark for object-centric spatial reasoning in foundation models. Using a controlled synthetic dataset, we evaluate state-of-the-art vision models (e.g., GroundingDINO, Florence-2, OWLv2) and large VLMs (e.g., InternVL, LLaVA, GPT-4o) across three tasks: spatial localization, spatial reasoning, and downstream retrieval tasks. We find a stable trade-off: detectors such as GroundingDINO and OWLv2 deliver precise boxes with limited relational reasoning, while VLMs like SmolVLM and GPT-4o provide coarse layout cues and fluent captions but struggle with fine-grained spatial context. Our study highlights the gap between localization and true spatial understanding, and pointing toward the need for spatially-aware foundation models in the community.

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