Explaining the ambiguity of object detection and 6D pose from visual data
Manhardt F, Arroyo DM, Rupprecht C, Busam B, Birdal T, Navab N, Tombari F (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2019-October
Pages Range: 6840-6849
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: Seoul, KOR
ISBN: 9781728148038
Abstract
3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetries, occlusion and repetitive textures. This ambiguity in both detection and pose estimation means that an object instance can be perfectly described by several different poses and even classes. In this work we propose to explicitly deal with these ambiguities. For each object instance we predict multiple 6D pose outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures. The distribution collapses to a single outcome when the visual appearance uniquely identifies just one valid pose. We show the benefits of our approach which provides not only a better explanation for pose ambiguity, but also a higher accuracy in terms of pose estimation.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Stanford University
United States (USA) (US)
University of Oxford
United Kingdom (GB)
Germany (DE)
Stanford University
United States (USA) (US)
University of Oxford
United Kingdom (GB)
How to cite
APA:
Manhardt, F., Arroyo, D.M., Rupprecht, C., Busam, B., Birdal, T., Navab, N., & Tombari, F. (2019). Explaining the ambiguity of object detection and 6D pose from visual data. In Proceedings of the IEEE International Conference on Computer Vision (pp. 6840-6849). Seoul, KOR: Institute of Electrical and Electronics Engineers Inc..
MLA:
Manhardt, Fabian, et al. "Explaining the ambiguity of object detection and 6D pose from visual data." Proceedings of the 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, KOR Institute of Electrical and Electronics Engineers Inc., 2019. 6840-6849.
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