Does HDR Pre-Processing Improve the Accuracy of 3D Models Obtained by Means of two Conventional SfM-MVS Software Packages? The Case of the Corral del Veleta Rock Glacier

Abstract

The accuracy of different workflows using Structure-from-Motion and Multi-View-Stereo techniques (SfM-MVS) is tested. Twelve point clouds of the Corral del Veleta rock glacier, in Spain, were produced with two different software packages (123D Catch and Agisoft Photoscan), using Low Dynamic Range images and High Dynamic Range compositions (HDR) for three different years (2011, 2012 and 2014). The accuracy of the resulting point clouds was assessed using benchmark models acquired every year with a Terrestrial Laser Scanner. Three parameters were used to estimate the accuracy of each point cloud: the RMSE, the Cloud-to-Cloud distance (C2C) and the Multiscale-Model-to-Model comparison (M3C2). The M3C2 mean error ranged from 0.084 m (standard deviation of 0.403 m) to 1.451 m (standard deviation of 1.625 m). Agisoft Photoscan overcome 123D Catch, producing more accurate and denser point clouds in 11 out 12 cases, being this work, the first available comparison between both software packages in the literature. No significant improvement was observed using HDR pre-processing. To our knowledge, this is the first time that the geometrical accuracy of 3D models obtained using LDR and HDR compositions are compared. These findings may be of interest for researchers who wish to estimate geomorphic changes using SfM-MVS approaches.

Keywords

• point clouds
• rock glacier
• Structure-from-Motion &amp
• Multi-View Stereo (SfM-MVS)
• High Dynamic Range (HDR)
• Low Dynamic Range (LDR)