系统梳理3D生成的表示、方法与评估，助力高效建模。
3D Shape Generation: A Survey
- 按显式、隐式、混合方式分类3D表示结构
- 总结主流生成架构与常用数据集及评价指标
- 适合想快速掌握3D生成全貌的研究者
深度学习的进展显著推动了3D形状生成领域的发展，实现了复杂、多样且语义明确的3D物体合成。本综述系统梳理了当前3D形状生成的前沿技术，围绕三大核心模块展开：形状表示、生成建模方法和评估协议。首先，将3D表示分为显式、隐式与混合三类，分析其结构特性、优劣；其次，回顾主流生成方法，聚焦前馈架构；再次，归纳常用数据集与评价指标，涵盖保真度、多样性与真实感；最后，指出开放挑战并展望可控性、效率与高质量生成的未来方向。本文旨在为研究人员与实践者提供结构化、深入的理解参考。
原文摘要 · Abstract (English)
Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state-of-the-art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.
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