梳理3D视觉的多种数据表示与学习范式，构建统一认知框架。
A Cookbook of 3D Vision: Data, Learning Paradigms, and Application
- 按点云、网格、体素、3D高斯等结构化分类3D数据表示
- 揭示数据集设计与监督方式对3D重建、生成和视频建模的影响
- 适合想系统理解3D视觉技术演进的科研人员和工程师
3D视觉快速发展，得益于多样化的数据表示、学习范式和建模策略。然而该领域在表示形式与基准测试间仍呈碎片化，难以形成对效率、保真度和可扩展性的统一认识。本文提出以数据为中心的3D视觉分类体系，将几何表示、数据集、学习框架与应用场景整合于同一概念图谱中。首先分析点云、网格、体素和3D高斯等核心3D数据结构及其采集流程；接着探讨数据集设计、基准构建与监督机制如何推动2D监督3D学习、隐式神经表示及4D世界建模等进展。通过这一综合视角，厘清了表示、学习范式与下游任务（重建、生成、视频建模）之间的关系，呈现了兼顾效率与保真度、迈向多模态几何对齐的新兴趋势。
原文摘要 · Abstract (English)
3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and modeling strategies. Yet the field remains fragmented across representations and benchmarks, making it difficult to develop unified perspectives on efficiency, fidelity, and scalability. This work provides a data-centric taxonomy of 3D vision that connects geometric representations, datasets, learning frameworks, and applications within a single conceptual map. We begin by analysing the principal structural representations of 3D data--point clouds, meshes, voxels, and 3D Gaussians--along with their acquisition pipelines. We then examine how dataset design, benchmark construction, and supervision regimes shape recent advances, spanning 2D-supervised 3D learning, implicit neural representations, and 4D world modeling. Through this integrative lens, we clarify the relationships among representations, learning paradigms, and downstream tasks in reconstruction, generation, and video modeling, offering a consolidated view of emerging trends toward balancing efficiency and fidelity and toward multimodal geometric grounding.
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