Centre dedocumentation
scientifique

Robust structure from motion based on relative rotations and tie points / Xin Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 5 (May 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 5 (May 2019) . - pp 347 - 359 Titre : Robust structure from motion based on relative rotations and tie points Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 347 - 359 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] compensation locale par faisceaux [Termes descripteurs IGN] équation linéaire [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] point de liaison (imagerie) [Termes descripteurs IGN] rotation [Termes descripteurs IGN] structure-from-motion Résumé : (Auteur) In this article, we present two new approaches for image orientation with a focus on robustness, starting with relative orientations of available image pairs, an incremental and a global one, and compare their performance. For the incremental approach, we first choose a suitable initial image pair, and we then iteratively extend the image cluster by adding new images. The rotations of these newly added images are estimated from relative rotations by single rotation averaging. In the next step, a linear equation system is set up for each new image to solve the translation parameters with triangulated tie points that can be viewed in that new image, followed by a resection for refinement. Finally, we refine the orientation parameters of the images by a local bundle adjustment. We also present a global method that consists of two parts: global rotation averaging, followed by setting up a large linear equation system to solve for all image translation parameters simultaneously; a final bundle adjustment is carried out to refine the results. We compare these two methods by analyzing results on different benchmark sets, including ordered and unordered image data sets from the Internet and two other challenging data sets to demonstrate the performance of our two approaches. We conclude that while the incremental method typically yields results of higher accuracy and performs better on the challenging data sets, our global method runs significantly faster. Numéro de notice : A2019-438 Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.5.347 date de publication en ligne : 01/05/2019 En ligne : https://doi.org/10.14358/PERS.85.5.347 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92769 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019051	SL	Revue	Centre de documentation	Revues en salle	Disponible

Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 19 - 41 Titre : Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] matrice de rotation [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] recouvrement d'images [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters. We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness. Numéro de notice : A2019-033 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.009 date de publication en ligne : 15/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91970 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019013	DEP-EXM	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019012	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt