Deriving 3D point clouds from terrestrial photographs - comparison of different sensors and softwareThis paper was published in 2016 in the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences in context with the ISPRS Congress 2016 in Prague. This research was performed during the International Summer School on Close-Range Sensing in alpine terrain 2015 in Tyrol (Austria). Download Paper
Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives.
We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (18.104.22.1686), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud.
While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.
Keywords: Close-range photogrammetry, structure from motion, dense matching, software
I enjoyed coauthoring this publication which resulted from my attendence of the International Summer School on Close-Range sensing in Alpine Terrain in 2015.
This research is a result of a close collaboration of Robert Niederheiser (Institute for Interdisciplinary Mountain Research – Austrian Academy of Sciences, Innsbruck, Austria), Martin Mokroš (Department of Forest Management and Geodesy, Technical University in Zvolen, Slovak Republic) and me (GIScience research group, Department of Geography, Friedrich Schiller University Jena, Germany), with the help of Department of Geography and Geology, University of Salzburg, Austria)aculty of Geo-Information Science and Earth Observation, Universiteit Twente, Enschede, Netherlands).