Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models

• URL: http://arxiv.org/abs/2510.13394v2
• Date: Thu, 23 Oct 2025 14:31:13 GMT
• Title: Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models
• Authors: Xinmiao Huang, Qisong He, Zhenglin Huang, Boxuan Wang, Zhuoyun Li, Guangliang Cheng, Yi Dong, Xiaowei Huang,
• Abstract summary: We propose a unified benchmark, textbfSpatial-DISE, based on a cognitively grounded taxonomy that categorizes tasks into four fundamental quadrants.<n>To address the issue of data scarcity, we develop a scalable and automated pipeline to generate diverse and verifiable spatial reasoning questions.
• Score: 21.28937516885804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, augmented reality, and autonomous navigation. Unfortunately, existing benchmarks are inadequate in assessing spatial reasoning ability, especially the \emph{intrinsic-dynamic} spatial reasoning which is a fundamental aspect of human spatial cognition. In this paper, we propose a unified benchmark, \textbf{Spatial-DISE}, based on a cognitively grounded taxonomy that categorizes tasks into four fundamental quadrants: \textbf{I}ntrinsic-\textbf{S}tatic, Intrinsic-\textbf{D}ynamic, \textbf{E}xtrinsic-Static, and Extrinsic-Dynamic spatial reasoning. Moreover, to address the issue of data scarcity, we develop a scalable and automated pipeline to generate diverse and verifiable spatial reasoning questions, resulting in a new \textbf{Spatial-DISE} dataset that includes Spatial-DISE Bench (559 evaluation VQA pairs) and Spatial-DISE-12K (12K+ training VQA pairs). Our comprehensive evaluation across 28 state-of-the-art VLMs reveals that, current VLMs have a large and consistent gap to human competence, especially on multi-step multi-view spatial reasoning. Spatial-DISE offers a robust framework, valuable dataset, and clear direction for future research toward human-like spatial intelligence. Benchmark, dataset, and code will be publicly released.

Related papers

• EscherVerse: An Open World Benchmark and Dataset for Teleo-Spatial Intelligence with Physical-Dynamic and Intent-Driven Understanding [56.89359230139883]
  We introduce Teleo-Spatial Intelligence (TSI), a new paradigm that unifies two critical pillars: Physical-Dynamic Reasoning and Intent-Driven Reasoning.<n>We present EscherVerse, consisting of a large-scale, open-world benchmark (Escher-Bench), a dataset (Escher-35k), and models (Escher series)<n>It is the first benchmark to systematically assess Intent-Driven Reasoning, challenging models to connect physical events to their underlying human purposes.
  arXiv  Detail & Related papers  (2026-01-04T14:42:39Z)
• SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition [19.526371771173064]
  spatial cognition is fundamental to real-world multimodal intelligence, allowing models to interact with the physical environment.<n>Existing benchmarks often oversimplify spatial cognition, reducing it to a single-dimensional metric.<n>We propose a hierarchical spatial cognition framework that decomposes spatial intelligence into five progressively complex levels.
  arXiv  Detail & Related papers  (2025-11-26T15:04:18Z)
• Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks [108.15756345836901]
  We provide a comprehensive review of multimodal spatial reasoning tasks with large models.<n>We review advances in embodied AI, including vision-language navigation and action models.<n>We consider emerging modalities such as audio and egocentric video, which contribute to novel spatial understanding through new sensors.
  arXiv  Detail & Related papers  (2025-10-29T17:55:43Z)
• How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspective [103.44502230776352]
  We present a systematic investigation of Visual Spatial Reasoning (VSR) in Vision-Language Models (VLMs)<n>We categorize spatial intelligence into three levels of capability, ie, basic perception, spatial understanding, spatial planning, and curate SIBench, a spatial intelligence benchmark encompassing nearly 20 open-source datasets across 23 task settings.
  arXiv  Detail & Related papers  (2025-09-23T12:00:14Z)
• ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models [68.46716645478661]
  Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and reasoning about visual content.<n>Current VLMs excel primarily at egocentric spatial reasoning (from the camera's perspective) but fail to generalize to allocentric viewpoints.<n>We introduce ViewSpatial-Bench, the first comprehensive benchmark designed specifically for multi-viewpoint spatial localization recognition evaluation.
  arXiv  Detail & Related papers  (2025-05-27T17:59:26Z)
• SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding [64.15606979785355]
  Multimodal large language models (MLLMs) have achieved impressive success in question-answering tasks, yet their capabilities for spatial understanding are less explored.<n>This work investigates a critical question: do existing MLLMs possess 3D spatial perception and understanding abilities?
  arXiv  Detail & Related papers  (2025-05-22T17:59:03Z)
• SITE: towards Spatial Intelligence Thorough Evaluation [121.1493852562597]
  Spatial intelligence (SI) represents a cognitive ability encompassing the visualization, manipulation, and reasoning about spatial relationships.<n>We introduce SITE, a benchmark dataset towards SI Thorough Evaluation.<n>Our approach to curating the benchmark combines a bottom-up survey about 31 existing datasets and a top-down strategy drawing upon three classification systems in cognitive science.
  arXiv  Detail & Related papers  (2025-05-08T17:45:44Z)
• Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models [14.442394137843923]
  We present a detailed analysis that first delineates the core elements of spatial reasoning.<n>We then assesses the performance of these models in both synthetic and real-world images.
  arXiv  Detail & Related papers  (2025-03-25T14:34:06Z)
• HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions [64.69468932145234]
  We present HA-VLN 2.0, a unified benchmark introducing explicit social-awareness constraints.<n>Results show that explicit social modeling improves navigation robustness and reduces collisions.
  arXiv  Detail & Related papers  (2025-03-18T13:05:55Z)
• SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation [7.659514491338669]
  Current vision-language models may grasp basic spatial cues but struggle with the multi-dimensional spatial reasoning necessary for human-like understanding and real-world applications.<n>We develop SPHERE, a hierarchical evaluation framework supported by a new human-annotated dataset.<n> Benchmark evaluation of state-of-the-art models reveals significant deficiencies, especially in reasoning about distance and proximity.
  arXiv  Detail & Related papers  (2024-12-17T09:10:55Z)
• Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning [4.422649561583363]
  We present a novel benchmark for assessing spatial reasoning in language models (LMs) It is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. A key contribution is our logic-based consistency-checking tool, which enables the assessment of multiple plausible solutions.
  arXiv  Detail & Related papers  (2024-05-23T21:22:00Z)
• SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities [59.39858959066982]
  understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. We develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
  arXiv  Detail & Related papers  (2024-01-22T18:01:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.