Inferring super-resolution depth from a moving light-source enhanced RGB-D Sensor: A variational approach
Sang L, Haefner B, Cremers D (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 1-10
Conference Proceedings Title: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Event location: Snowmass Village, CO, USA
ISBN: 9781728165530
DOI: 10.1109/WACV45572.2020.9093491
Abstract
A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects from multiple viewpoints with unknown motion. This non-static camera-to-object setup is described with a nonconvex variational approach such that no calibration on lighting or camera motion is required due to the formulation of an end-to-end joint optimization problem. Solving the proposed variational model results in high resolution depth, reflectance and camera pose estimates, as we show on challenging synthetic and real-world datasets.
Involved external institutions
Germany (DE)How to cite
APA:
Sang, L., Haefner, B., & Cremers, D. (2020). Inferring super-resolution depth from a moving light-source enhanced RGB-D Sensor: A variational approach. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1-10). Snowmass Village, CO, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Sang, Lu, Bjoern Haefner, and Daniel Cremers. "Inferring super-resolution depth from a moving light-source enhanced RGB-D Sensor: A variational approach." Proceedings of the 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020, Snowmass Village, CO, USA Institute of Electrical and Electronics Engineers Inc., 2020. 1-10.
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