ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 191-198, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-191-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
30 May 2017
AN EFFICIENT METHOD TO DETECT MUTUAL OVERLAP OF A LARGE SET OF UNORDERED IMAGES FOR STRUCTURE-FROM-MOTION
X. Wang1, Z.Q. Zhan2, and C. Heipke1 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
2School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430072, People’s Republic of China
Keywords: unordered set of images, image orientation, random k-d forest Abstract. Recently, low-cost 3D reconstruction based on images has become a popular focus of photogrammetry and computer vision research. Methods which can handle an arbitrary geometric setup of a large number of unordered and convergent images are of particular interest. However, determining the mutual overlap poses a considerable challenge.

We propose a new method which was inspired by and improves upon methods employing random k-d forests for this task. Specifically, we first derive features from the images and then a random k-d forest is used to find the nearest neighbours in feature space. Subsequently, the degree of similarity between individual images, the image overlaps and thus images belonging to a common block are calculated as input to a structure-from-motion (sfm) pipeline. In our experiments we show the general applicability of the new method and compare it with other methods by analyzing the time efficiency. Orientations and 3D reconstructions were successfully conducted with our overlap graphs by sfm. The results show a speed-up of a factor of 80 compared to conventional pairwise matching, and of 8 and 2 compared to the VocMatch approach using 1 and 4 CPU, respectively.

Conference paper (PDF, 1124 KB)


Citation: Wang, X., Zhan, Z. Q., and Heipke, C.: AN EFFICIENT METHOD TO DETECT MUTUAL OVERLAP OF A LARGE SET OF UNORDERED IMAGES FOR STRUCTURE-FROM-MOTION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 191-198, https://doi.org/10.5194/isprs-annals-IV-1-W1-191-2017, 2017.

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