The use of Remotely Piloted Aircraft Systems (RPAS) (Colomina, Molina, 2014 Nex, Remondino, 2014 Campana, 2017) and masts (Georgopoulos, 1999 Mozas-Calvache et al., 2012 Martínez et al. 2020 Bertellini et al., 2019) were implemented in order to reduce the quantity of images needed to cover the object. 2017 Barazzetti et al., 2017b) and 360 degrees cameras (Pérez-Ramos, Robleda-Prieto, 2016 Kossieris et al., 2017 Mandelli et al., 2017 Barazzetti et al., 2017a Fangi et al., 2018 Cantatore et al. So some alternative procedures, such as the use of wide angle lens (Gómez-Lahoz, González-Aguilera, 2009 Mozas et al., 2012 Martínez et al., 2013 Fiorillo et al., 2016), fisheye lens (Boulianne et al., 1997 Kedzierski, Waczykowski, 2007 Georgantas et al., 2012 Covas et al., 2015 Perfetti et al.
In addition, the presence of narrow spaces or occlusions could increase the quantity of photographs needed to cover the structure using normal focal lenses mounted in non-metric cameras. We conclude this paper by pointing out current and future trends. We also compile and discuss commonly adopted datasets and figures of merit indicated for each purpose and list recent results for completeness. 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). The classical stereo matching is then revised on the spherical domain, where methodologies for detecting and describing sparse and dense features become crucial. We then survey monocular layout and depth inference approaches, highlighting the recent advances in learning-based solutions suited for spherical data. 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. 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 omnidirectional optics.
A calibration aimed at understanding the intrinsic parameters of the two lenses camera, as well as their relative orientation, allowed one to generate new equirectangular projections from which a significant improvement of geometric accuracy has been achieved.
As results were in contrast with the accuracy achieved by using the original fisheye images (front and rear facing images) in photogrammetric reconstructions, an alternative solution to generate the equirectangular projections was developed.
#PTGUI STITCH SAMSUNG 360 SOFTWARE#
Results demonstrate that the direct use of the projection generated inside the mobile phone or with Gear 360 Action Direction (the desktop software for post-processing) have a relatively low metric accuracy. The aim of this work is to test the metric accuracy and the level of detail achievable with the Samsung Gear 360 coupled with digital modelling techniques based on photogrammetry/computer vision algorithms. The Samsung Gear 360 is a consumer grade spherical camera able to capture photos and videos.
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