Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images
Tateno K, Navab N, Tombari F (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11220 LNCS
Pages Range: 732-750
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783030012694
DOI: 10.1007/978-3-030-01270-0_43
Abstract
There is a high demand of 3D data for 360 panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e.g., omni-directional cameras) as well as visualizing in 3D (e.g., head mounted displays) panoramic images and videos. At the same time, 3D sensors able to capture 3D panoramic data are expensive and/or hardly available. To fill this gap, we propose a learning approach for panoramic depth map estimation from a single image. Thanks to a specifically developed distortion-aware deformable convolution filter, our method can be trained by means of conventional perspective images, then used to regress depth for panoramic images, thus bypassing the effort needed to create annotated panoramic training dataset. We also demonstrate our approach for emerging tasks such as panoramic monocular SLAM, panoramic semantic segmentation and panoramic style transfer.
Involved external institutions
Germany (DE)How to cite
APA:
Tateno, K., Navab, N., & Tombari, F. (2018). Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images. In Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 732-750). Munich, DEU: Springer Verlag.
MLA:
Tateno, Keisuke, Nassir Navab, and Federico Tombari. "Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images." Proceedings of the 15th European Conference on Computer Vision, ECCV 2018, Munich, DEU Ed. Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert, Springer Verlag, 2018. 732-750.
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