This article provides a comprehensive survey on pioneer and state-of-the-art 3D scene geometry estimation methodologies based on single, two, or multiple images captured under omnidirectional optics. We first revisit the basic concepts of the spherical camera model and review the most common acquisition technologies and representation formats suitable for omnidirectional (also called 360°, spherical or panoramic) images and videos. We then survey monocular layout and depth inference approaches, highlighting the recent advances in learning-based solutions suited for spherical data. The classical stereo matching is then revised on the spherical domain, where methodologies for detecting and describing sparse and dense features become crucial. The stereo matching concepts are then extrapolated for multiple view camera setups, categorizing them among light fields, multi-view stereo, and structure from motion (or visual simultaneous localization and mapping). We also compile and discuss commonly adopted datasets and figures of merit indicated for each purpose and list recent results for completeness. We conclude this article by pointing out current and future trends.

本文对基于在全向光学下捕获的单个、两个或多个图像的先驱和最先进的 3D 场景几何估计方法进行了全面调查。我们首先回顾了球形相机模型的基本概念，并回顾了最常见的适用于全向（也称为 360°、球形或全景）图像和视频的采集技术和表示格式。然后，我们调查了单眼布局和深度推理方法，强调了适用于球形数据的基于学习的解决方案的最新进展。然后在球域上修改经典立体匹配，其中检测和描述稀疏和密集特征的方法变得至关重要。然后将立体匹配概念外推到多视图相机设置，将它们分类为光场、多视图立体和运动结构（或视觉同步定位和映射）。我们还编译和讨论了普遍采用的数据集和为每个目的指出的品质因数，并列出了最近的结果以确保完整性。我们通过指出当前和未来的趋势来结束本文。