高斯泼溅：三维重建与新视角合成综述
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review
AI总结：
本文综述了基于高斯泼溅的三维重建与新视角合成技术，系统梳理其输入类型、模型结构、输出表示和训练策略，并探讨现存挑战与未来方向。
摘要：
基于图像的三维重建是一项具有挑战性的任务，涉及从一组输入图像中推断物体或场景的三维形状。基于学习的方法因其能够直接估计三维形状而受到关注。本综述论文聚焦于三维重建的最新技术，包括生成新颖的、未见过的视角。文中概述了高斯泼溅方法的最新进展，涵盖输入类型、模型结构、输出表示和训练策略。同时讨论了尚未解决的挑战和未来方向。鉴于该领域进展迅速，并且存在大量改进三维重建方法的机会，对相关算法进行全面审视显得至关重要。因此，本研究对高斯泼溅的最新进展进行了详尽概述。
英文摘要：
Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have gained attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art techniques for 3D reconstruction, including the generation of novel, unseen views. An overview of recent developments in the Gaussian Splatting method is provided, covering input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this domain and the numerous opportunities for enhancing 3D reconstruction methods, a comprehensive examination of algorithms appears essential. Consequently, this study offers a thorough overview of the latest advancements in Gaussian Splatting.
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。
