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.
基于图像的三维重建是一项挑战性任务，涉及从一组输入图像中推断出对象或场景的三维形状。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、未见过的视图。文章提供了高斯喷溅方法近期发展的概览，涵盖输入类型、模型结构、输出表达和训练策略。同时讨论了尚未解决的挑战和未来的发展方向。鉴于该领域的快速进展和增强三维重建方法的众多机会，对算法进行全面审查显得尤为重要。因此，本研究提供了关于高斯喷溅最新进展的详尽概述。