3D Scene Geometry Estimation from 360$^\circ$ Imagery: A Survey
Thiago Lopes Trugillo da Silveira Paulo Gamarra Lessa Pinto Jeffri Erwin Murrugarra Llerena Claudio Rosito Jung
This paper 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 the 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$^\circ$, 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 paper by
pointing out current and future trends.