Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143225
Title: Salience guided depth calibration for perceptually optimized compressive light field 3D display
Authors: Wang, Shizheng
Liao, Wenjuan
Surman, Phil
Tu, Zhigang
Zheng, Yuanjin
Yuan, Junsong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Wang, S., Liao, W., Surman, P., Tu, Z., Zheng, Y., & Yuan, J. (2018). Salience guided depth calibration for perceptually optimized compressive light field 3D display. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018 CVPR), 2031-2040. doi:10.1109/CVPR.2018.00217
Abstract: Multi-layer light field displays are a type of computational three-dimensional (3D) display which has recently gained increasing interest for its holographic-like effect and natural compatibility with 2D displays. However, the major shortcoming, depth limitation, still cannot be overcome in the traditional light field modeling and reconstruction based on multi-layer liquid crystal displays (LCDs). Considering this disadvantage, our paper incorporates a salience guided depth optimization over a limited display range to calibrate the displayed depth and present the maximum area of salience region for multi-layer light field display. Different from previously reported cascaded light field displays that use the fixed initialization plane as the depth center of display content, our method automatically calibrates the depth initialization based on the salience results derived from the proposed contrast enhanced salience detection method. Experiments demonstrate that the proposed method provides a promising advantage in visual perception for the compressive light field displays from both software simulation and prototype demonstration.
URI: https://hdl.handle.net/10356/143225
ISBN: 978-1-5386-6421-6
DOI: 10.1109/CVPR.2018.00217
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CVPR.2018.00217.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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