mlsanityData to 7 Oct 2026

GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models

arXiv 2412.21036 ↗First posted 30 Dec 2024cs.CLcited by 1

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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 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 current state-of-the-art MLLMs exhibit significant deficiencies in geometric perception tasks. Furthermore, we show that models trained with GePBench data demonstrate considerable improvements on a wide range of downstream tasks, highlighting the critical role of geometric perception in enabling advanced multimodal applications. Our code and datasets are available at hrefhttps://github.com/Changhao-Xiang/GePBenchhttps://github.com/Changhao-Xiang/GePBench.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 49 of its 81 references that are in the map. First posted on arXiv 30 Dec 2024.

Builds on

49 of 81 references.

References · 49

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37 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026GeoChallenge: A Multi-Answer Multiple-Choice Benchmark for Geometric Reasoning with Diagramscited by 1

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