arXiv:2412.21036v3 Announce Type: replace Abstract: Geometric shapes play important roles in both physical world and human cognition. While multimodal large language models (MLLMs) have made significant advancements in visual understanding, their abilities to recognize geometric shapes and their spatial relationships, which we term \emph{geometric perception}, are not explicitly and systematically explored. To address this gap, we introduce GePBench, a novel benchmark specifically designed to assess the geometric perception capabilities of MLLMs. Our extensive evaluations reveal that even the
The release of GePBench indicates a critical juncture in MLLM development where foundational geometric perception, previously implicitly assumed, is now being explicitly tested.
This benchmark highlights a significant gap in MLLM capabilities, suggesting that current models may lack true spatial reasoning, which is crucial for real-world applications requiring nuanced environmental interaction.
The explicit evaluation of geometric perception will drive MLLM research towards more robust spatial understanding rather than purely linguistic or superficial visual pattern recognition.
- · AI researchers in geometry and perception
- · MLLM developers focusing on foundational capabilities
- · Robotics and autonomous systems sectors
- · MLLMs with poor geometric reasoning
- · Applications relying on weak MLLM spatial understanding hypotheses
The benchmark will likely spur immediate research and development efforts to improve geometric perception in MLLMs.
Enhanced geometric perception could lead to more reliable and safer autonomous systems and advanced human-computer interaction.
Improved MLLM spatial intelligence could fundamentally alter design, engineering, and manufacturing processes, enabling more intuitive and precise AI-assisted creation.
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