基于学习的3D表示的最新进展与趋势
Recent Advances and Trends in Learning-based 3D Representations
AI总结：
本文综述了从离散显式格式到连续隐式场（基于神经渲染或基元溅射）的3D表示家族，分析了其优缺点及关键应用，并强调了向隐式表示的范式转变。
摘要：
选择合适的3D表示是一个基本的设计决策，它决定了现代计算机视觉和图形管线在3D重建、新视角合成与渲染、形状与运动分析、识别和生成等任务中的效率、质量和能力。虽然传统表示（如网格、点云和体素网格）仍然是3D传感器（如LiDAR和3D扫描仪）的标准输出，并广泛应用于下游应用（如编辑和仿真），但最近的神经和基元表示（如3D高斯溅射）提供了紧凑且可微的替代方案，在游戏、AR/VR、自动驾驶、机器人导航和医学成像等应用中开辟了广泛的机会。本文的目标是综述主要的3D表示家族，从离散显式格式到基于神经渲染或基元溅射的连续隐式场。对于每种表示类型，我们介绍其一般公式和变体，讨论其优点和局限性，并突出关键应用。最后，我们概述了开放挑战和未来研究的潜在方向。与近期广泛涵盖3D物体和场景重建的综述不同，本文专注于分析3D表示本身的演变。我们特别强调了向隐式表示的范式转变，提供了关于这些新兴格式如何从根本上改变3D/4D工作流程的新视角。
英文摘要：
The selection of an appropriate 3D representation is a fundamental design decision that dictates the efficiency, quality, and capabilities of modern computer vision and graphics pipelines for tasks such as 3D reconstruction, novel-view synthesis and rendering, shape and motion analysis, recognition, and generation. While traditional representations (\eg meshes, point clouds, and volumetric grids) remain standard outputs of 3D sensors (\eg LiDAR and 3D scanners) and are widely used in downstream applications (\eg editing and simulation), recent neural and primitive-based representations (\eg 3D Gaussian Splatting) offer compact and differentiable alternatives opening a wide range of opportunities in applications such as games, AR/VR, autonomous driving, robot navigation, and medical imaging, to name a few. The goal of this paper is to survey the main families of 3D representations from discrete explicit formats to continuous implicit fields based either on neural rendering or primitive splatting. For each type of representation, we present the general formulation and its variants, discuss its benefits and limitations, and highlight key applications. We conclude the paper by outlining the open challenges and potential directions for future research. Distinct from recent surveys that broadly cover 3D object and scene reconstruction, this paper provides a focused analysis on the evolution of 3D representations themselves. We specifically emphasize the paradigm shift toward implicit representations, offering a novel perspective on how these emerging formats fundamentally alter 3D/4D workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
