Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161805
Title: What does plate glass reveal about camera calibration?
Authors: Zheng, Qian
Chen, Jinnan
Lu, Zhan
Shi, Boxin
Jiang, Xudong
Yap, Kim-Hui
Duan, Ling-Yu
Kot, Alex Chichung
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Zheng, Q., Chen, J., Lu, Z., Shi, B., Jiang, X., Yap, K., Duan, L. & Kot, A. C. (2020). What does plate glass reveal about camera calibration?. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3019-3029. https://dx.doi.org/10.1109/CVPR42600.2020.00309
Project: NRF2016NRF-NSFC001-098 
NTU-PKU 
metadata.dc.contributor.conference: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Abstract: This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image contents. To reduce the impact of a noisy calibration cue estimated from a reflection-contaminated image, we propose two strategies: an optimization-based method that imposes part of though reliable entries on the map and a learning-based method that fully exploits all entries. We collect a dataset containing 320 samples as well as their camera parameters for evaluation. We demonstrate that our method not only facilitates a general single image camera calibration method that leverages image contents but also contributes to improving the performance of single image reflection removal. Furthermore, we show our byproduct output helps alleviate the ill-posed problem of estimating the panorama from a single image.
URI: https://hdl.handle.net/10356/161805
URL: https://openaccess.thecvf.com/menu
ISBN: 978-1-7281-7168-5
DOI: 10.1109/CVPR42600.2020.00309
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 The Author(s). This CVPR 2020 paper is the Open Access version, provided by the Computer Vision Foundation.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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