GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models

• URL: http://arxiv.org/abs/2412.21036v2
• Date: Sun, 16 Feb 2025 07:39:45 GMT
• Title: GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models
• Authors: Shangyu Xing, Changhao Xiang, Yuteng Han, Yifan Yue, Zhen Wu, Xinyu Liu, Zhangtai Wu, Fei Zhao, Xinyu Dai,
• Abstract summary: We introduce GePBench, a novel benchmark designed to assess the geometric perception abilities of MLLMs. Our evaluations reveal that current state-of-the-art MLLMs exhibit significant deficiencies in geometric perception tasks. We show that models trained with GePBench data demonstrate substantial improvements on a wide range of benchmark tasks.
• Score: 34.647839550142834
• License:
• Abstract: Multimodal large language models (MLLMs) have made significant progress in integrating visual and linguistic understanding. Existing benchmarks typically focus on high-level semantic capabilities, such as scene understanding and visual reasoning, but often overlook a crucial, foundational ability: geometric perception. Geometric perception involves understanding geometric shapes, structures, and spatial relationships, which are essential for supporting higher-level semantic tasks. Despite its importance, this capability remains underexplored in current MLLM research. To address this gap, we introduce GePBench, a novel benchmark designed to assess the geometric perception abilities of MLLMs. Our extensive evaluations reveal that current state-of-the-art MLLMs exhibit significant deficiencies in geometric perception tasks. Furthermore, we show that models trained with GePBench data demonstrate substantial improvements on a wide range of benchmark tasks, highlighting the critical role of geometric perception in enabling advanced multimodal applications. Our code and datasets will be publicly available.

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