3D和4D世界建模：一项综述
3D and 4D World Modeling: A Survey
- WorldBench Team（WorldBench团队）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该综述针对世界建模中对3D和4D表示利用不足及定义分类缺失的问题，通过建立精确概念、引入结构化分类法，系统总结相关数据集和评估指标，讨论应用、挑战与方向，为3D和4D世界建模领域提供基础参考。
AI中文摘要：
世界建模已成为人工智能研究的基石，使智能体能够理解、表示和预测它们所处的动态环境。先前工作主要强调二维图像和视频数据的生成方法，却忽视了利用RGB-D图像、占用网格和激光雷达点云等原生三维和四维表示进行大规模场景建模的大量工作。同时，“世界模型”缺乏标准化定义和分类法，导致文献中的观点零散且有时不一致。本综述通过首次全面回顾专门致力于三维和四维世界建模与生成来解决这些差距。我们建立了精确的定义，引入了涵盖基于视频（VideoGen）、基于占用（OccGen）和基于激光雷达（LiDARGen）方法的结构化分类法，并系统地总结了针对三维/四维设置的数据集和评估指标。我们还讨论了实际应用，确定了开放挑战，并突出了有前景的研究方向，旨在为推进该领域提供连贯且基础的参考。现有文献的系统总结可在该https网址获取。
英文摘要：
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphasizes generative methods for 2D image and video data, they overlook the rapidly growing body of work that leverages native 3D and 4D representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds for large-scale scene modeling. At the same time, the absence of a standardized definition and taxonomy for "world models" has led to fragmented and sometimes inconsistent claims in the literature. This survey addresses these gaps by presenting the first comprehensive review explicitly dedicated to 3D and 4D world modeling and generation. We establish precise definitions, introduce a structured taxonomy spanning video-based (VideoGen), occupancy-based (OccGen), and LiDAR-based (LiDARGen) approaches, and systematically summarize datasets and evaluation metrics tailored to 3D/4D settings. We further discuss practical applications, identify open challenges, and highlight promising research directions, aiming to provide a coherent and foundational reference for advancing the field. A systematic summary of existing literature is available at https://github.com/worldbench/awesome-3d-4d-world-models
