系统梳理3D表示方法，涵盖主流技术与未来方向
3D Representation Methods: A Survey
- 按体素、点云、网格等类型分类梳理主流3D表示方法
- 对比分析NeRF、3D高斯泼溅等前沿方法的优劣
- 适合计算机视觉、图形学研究者参考
3D表示领域因计算机图形学、虚拟现实和自动驾驶等应用对高保真3D模型的需求而取得显著进展。本文综述了3D表示方法的发展历程与当前状态，重点分析其研究脉络、创新点及优缺点。涵盖体素网格、点云、网格、符号距离函数（SDF）、神经辐射场（NeRF）、3D高斯泼溅、三平面表示和深度分段四面体（DMTet）等关键技术。同时介绍了推动该领域发展的核心数据集，阐明其特征与研究影响。最后探讨了具有前景的研究方向，以进一步拓展3D表示方法的能力与应用场景。
原文摘要 · Abstract (English)
The field of 3D representation has experienced significant advancements, driven by the increasing demand for high-fidelity 3D models in various applications such as computer graphics, virtual reality, and autonomous systems. This review examines the development and current state of 3D representation methods, highlighting their research trajectories, innovations, strength and weakness. Key techniques such as Voxel Grid, Point Cloud, Mesh, Signed Distance Function (SDF), Neural Radiance Field (NeRF), 3D Gaussian Splatting, Tri-Plane, and Deep Marching Tetrahedra (DMTet) are reviewed. The review also introduces essential datasets that have been pivotal in advancing the field, highlighting their characteristics and impact on research progress. Finally, we explore potential research directions that hold promise for further expanding the capabilities and applications of 3D representation methods.
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