Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation
Kehl W, Milletari F, Tombari F, Ilic S, Navab N (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9907 LNCS
Pages Range: 205-220
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Amsterdam, NLD
ISBN: 9783319464862
DOI: 10.1007/978-3-319-46487-9_13
Abstract
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. During testing, scene patch descriptors are matched against a database of synthetic model view patches and cast 6D object votes which are subsequently filtered to refined hypotheses. We evaluate on three datasets to show that our method generalizes well to previously unseen input data, delivers robust detection results that compete with and surpass the state-of-the-art while being scalable in the number of objects.
Involved external institutions
Germany (DE)How to cite
APA:
Kehl, W., Milletari, F., Tombari, F., Ilic, S., & Navab, N. (2016). Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation. In Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 205-220). Amsterdam, NLD: Springer Verlag.
MLA:
Kehl, Wadim, et al. "Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation." Proceedings of the 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, NLD Ed. Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling, Springer Verlag, 2016. 205-220.
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