OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models (2506.03135v1)

Abstract: Spatial reasoning is a key aspect of cognitive psychology and remains a major bottleneck for current vision-LLMs (VLMs). While extensive research has aimed to evaluate or improve VLMs' understanding of basic spatial relations, such as distinguishing left from right, near from far, and object counting, these tasks represent only the most fundamental level of spatial reasoning. In this work, we introduce OmniSpatial, a comprehensive and challenging benchmark for spatial reasoning, grounded in cognitive psychology. OmniSpatial covers four major categories: dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking, with 50 fine-grained subcategories. Through Internet data crawling and careful manual annotation, we construct over 1.5K question-answer pairs. Extensive experiments show that both open- and closed-source VLMs, as well as existing reasoning and spatial understanding models, exhibit significant limitations in comprehensive spatial understanding. We further analyze failure cases and propose potential directions for future research.

• The paper introduces a comprehensive benchmark categorizing spatial tasks into four dimensions with 50 distinct subcategories.
• It compiles over 1,500 curated question-answer pairs from diverse real-world sources to push beyond basic spatial relationship identification.
• The evaluation reveals significant performance gaps between current VLMs and human-level spatial reasoning, proposing enhancements like PointGraph and SpatialCoT.

Understanding Comprehensive Spatial Reasoning in Vision-LLMs with OmniSpatial

Spatial reasoning remains a profound challenge in the development of Vision-LLMs (VLMs), pivotal for applications that necessitate visual perception linked to spatial understanding, like robotic manipulation and autonomous navigation. The paper "OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision LLMs" introduces a novel framework to advance the spatial reasoning capabilities of VLMs through the development of the OmniSpatial benchmark.

Core Contributions

1. Benchmark Development: OmniSpatial is designed as a comprehensive benchmark addressing limitations in current spatial reasoning evaluations. Explicitly, it incorporates fundamental advances by categorizing spatial tasks into four dimensions: dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking, providing a well-rounded assessment platform with 50 distinct subcategories.
2. Dataset Composition: The dataset encapsulates over 1,500 curated question-answer pairs derived from diverse sources, including web imagery, standardized cognitive tests, and real-world traffic scenarios. This extensive compilation aims to challenge VLMs beyond basic spatial relationship identification, introducing complex spatial scenarios that mirror real-life situations.
3. Evaluation of Current Models: OmniSpatial extensively evaluates both open and closed-source VLMs, revealing significant limitations in current models’ comprehensive spatial understanding. The experiments report performance metrics indicating a major gap between state-of-the-art models and human-level cognition, particularly in multilevel spatial reasoning tasks.
4. Proposals for Enhancement: The paper suggests novel methodologies to enhance VLM spatial reasoning capabilities, such as integrating PointGraph to emphasize object relations within visual scenes and the SpatialCoT approach for stimulating spatial imagination through novel view synthesis. These techniques are presented as avenues for improving spatial cognition models.

Insights from the OmniSpatial Benchmark

OmniSpatial delineates clear performance benchmarks for existing technologies, facilitating targeted improvements in VLMs. It highlights the efficiency and limitations of proprietary models (e.g., ChatGPT o3 and Gemini-2.5-Pro) alongside open-source VLMs in handling multifaceted spatial tasks. The findings confirm that current VLMs perform substantially below human capabilities, particularly in tasks involving geometric reasoning and perspective-taking.

The benchmark's structure informs the development of future spatial cognition models by identifying critical shortfalls and showcasing the potential impact of enhanced scene-grounded learning. Notably, the incorporation of structured object representation (such as PointGraph) is shown to elevate model performance in dynamic reasoning and perspective tasks, emphasizing the significance of nuanced spatial cues in improving model accuracy.

Future Directions

Considering the insights OmniSpatial provides, several areas for improvement emerge. The development of next-generation models could benefit from integrating explicit 3D spatial data and perspective-shifting training regimes akin to human cognitive processes. Furthermore, exploring reinforcement learning-based frameworks with enhanced multi-step reasoning could bridge the gap between current model outputs and human spatial cognition performance.

In theoretical terms, the research opens pathways for delving deeper into the intersection between vision and language processing, where cognitive spatial understanding is pivotal. Practical implications extend to enhanced AI systems with better-equipped navigation, robotic control, and interaction capabilities with their environments.

In conclusion, OmniSpatial provides a comprehensive evaluation framework that advances understanding in spatial reasoning capabilities within VLMs, laying vital groundwork for the development of more sophisticated and capable AI models tailored for real-world applications.