Open-Source Image-Based 3d Reconstruction Pipelines: Review, Comparison and Evaluation

Abstract

Abstract. State-of-the-art automated image orientation (Structure from Motion) and dense image matching (Multiple View Stereo) methods commonly used to produce 3D information from 2D images can generate 3D results – such as point cloud or meshes – of varying geometric and visual quality. Pipelines are generally robust and reliable enough, mostly capable to process even large sets of unordered images, yet the final results often lack completeness and accuracy, especially while dealing with real-world cases where objects are typically characterized by complex geometries and textureless surfaces and obstacles or occluded areas may also occur. In this study we investigate three of the available commonly used open-source solutions, namely COLMAP, OpenMVG+OpenMVS and AliceVision, evaluating their results under diverse large scale scenarios. Comparisons and critical evaluation on the image orientation and dense point cloud generation algorithms is performed with respect to the corresponding ground truth data. The presented FBK-3DOM datasets are available for research purposes.

Virtual Reality Models Based on Photogrammetric Surveys—A Case Study of the Iconostasis of the Serbian Orthodox Cathedral Church of Saint Nicholas in Sremski Karlovci (Serbia)

“…SfM is a method for photogrammetric surveying in which the determination of camera positions and orientation parameters are done automatically. Nowadays, there is a variety of image-based modeling software based on the SfM, such as the open-source ones: VisualSFM, AliceVision, OpenMVS, COLMAP, and Regard3D; and the popular commercial solutions: Agisoft Metashape (new version of Agisoft PhotoScan), Reality Capture, Autodesk Recap Photo, and Context Capture [12,[30][31][32].…”

“…The output of the image alignment process is an object sparse point cloud, followed by the estimated camera positions. The difference between the 3D position of the estimated tie point, and its re-projected point position on an image (RMS reprojection error) can be used as one of the metrics for evaluating the SfM process [31]. The sparse point cloud can be optionally optimized in terms of the removal of low-quality tie points, either manually or automatically.…”

“…The next step implies building mesh from the object dense cloud data. The new release of the Agisoft PhotoScan successor software -the Metashape from 2019, is the most accurate low-cost solution [31,32] and it is possible to create mesh directly from depth maps data, without the need to produce dense point cloud previously [39]. The calculation of depth-map for each image is the intermediate step of the processing, whereas the generated depth maps present the source data for creating a dense cloud and for the new meshing method [40].…”

Applying photogrammetry to reconstruct 3D luminance point clouds of indoor environments

“…architectural photogrammetry) is nearly as old as photography itself (Albota, 1976). From the 1990s, digital photogrammetry enabled automatic image measurements, camera calibration and exterior orientation (Haggrén & Niini, 1990;Heikkilä & Silven, 1997;Lowe, 1999;Pollefeys, Koch, & Van Gool, 1999;Stathopoulou, Welponer, & Remondino, 2019). Current photogrammetric software can automatically reconstruct 3D mesh models (Furukawa, Curless, Seitz, & Szeliski, 2009;Jancosek & Pajdla, 2011;Romanoni, Delaunoy, Pollefeys, & Matteucci, 2016).…”

A Versatile Multi-Camera System for 3d Acquisition and Modeling

“…Objects can be placed on a moving turning table and recorded from fixed sensor positions in a programmed pose sequence (Santos et al, 2014;Gattet et al, 2015;Hosseininaveh et al, 2015;Menna et al, 2017). Open-source (Stathopoulou et al, 2019) or commercial 3D reconstruction tools can then be utilized to process the collected data and derive 3D surface models.…”