Geometry problem solving, a crucial aspect of mathematical reasoning, is vital across various domains, including education, the assessment of AI's mathematical abilities, and multimodal capability evaluation. The recent surge in deep learning technologies, particularly the emergence of multimodal large language models, has significantly accelerated research in this area. This paper presents a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of state-of-the-art performance, existing challenges, and promising future directions. Our objective is to offer a comprehensive and practical reference of deep learning for geometry problem solving, thereby fostering further advancements in this field. We maintain a list of relevant papers: https://github.com/majianz/dl4gps.

翻译：几何问题求解作为数学推理的关键组成部分，在多个领域——包括教育、人工智能数学能力评估以及多模态能力评测——中具有至关重要的作用。近年来深度学习技术的迅猛发展，特别是多模态大语言模型的涌现，极大地推动了该领域的研究进展。本文对深度学习在几何问题求解中的应用进行了系统综述，涵盖以下四个方面：(i) 几何问题求解相关任务的全面总结；(ii) 相关深度学习方法的详尽梳理；(iii) 评估指标与评估方法的深入分析；以及(iv) 对当前最优性能、现存挑战及未来有前景方向的关键讨论。本文旨在为深度学习驱动的几何问题求解提供一份全面且实用的参考，从而推动该领域的进一步发展。我们维护了相关论文列表：https://github.com/majianz/dl4gps。