Structure from motion by artificial neural networks
Autor(en): | Schöning, J. Behrens, T. Faion, P. Kheiri, P. Heidemann, G. Krumnack, U. |
Herausgeber: | Sharma, P. Bianchi, F.M. |
Stichwörter: | Complex networks; Computer aided design; Image analysis; Neural networks; Neurons; Statistical tests; Three dimensional computer graphics, 3D reconstruction; 3D shape reconstruction; Artificial neuronal network; Multiple view geometry; Real-world image; Reconstructed objects; Structure from motion; Training and testing, Image reconstruction | Erscheinungsdatum: | 2017 | Herausgeber: | Springer Verlag | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Volumen: | 10269 LNCS | Startseite: | 146 | Seitenende: | 158 | Zusammenfassung: | Retrieving the 3D shape of an object from a collection of images or a video is currently realized with multiple view geometry algorithms, most commonly Structure from Motion (SfM) methods. With the aim of introducing artificial neuronal networks (ANN) into the domain of image-based 3D reconstruction of unknown object categories, we developed a scalable voxel-based dataset in which one can choose different training and testing subsets. We show that image-based 3D shape reconstruction by ANNs is possible, and we evaluate the aspect of scalability by examining the correlation between the complexity of the reconstructed object and the required amount of training samples. Along with our dataset, we are introducing, in this paper, a first baseline achieved by an only five-layer ANN. For capturing life's complexity, the ANNs trained on our dataset can be used a as pre-trained starting point and adapted for further investigation. Finally, we conclude with a discussion of open issues and further work empowering 3D reconstruction on real world images or video sequences by a CAD-model based ANN training data set. © Springer International Publishing AG 2017. |
Beschreibung: | Conference of 20th Scandinavian Conference on Image Analysis, SCIA 2017 ; Conference Date: 12 June 2017 Through 14 June 2017; Conference Code:192299 |
ISBN: | 9783319591254 | ISSN: | 03029743 | DOI: | 10.1007/978-3-319-59126-1_13 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020447538&doi=10.1007%2f978-3-319-59126-1_13&partnerID=40&md5=929887dafa77593d8daddd78c6a6c1f1 |
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