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A Weighted Optimization Approach to Time-of-Flight Sensor Fusion
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)ORCID iD: 0000-0002-2578-7896
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Realistic3D)
2014 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 23, no 1, 214-225 p.Article in journal (Refereed) Published
Abstract [en]

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2014. Vol. 23, no 1, 214-225 p.
Keyword [en]
Sensor fusion, range data, time-of-flight sensors, depth map upscaling, 3D video, stereo vision
National Category
Signal Processing
URN: urn:nbn:se:miun:diva-20415DOI: 10.1109/TIP.2013.2287613ISI: 000329195500017ScopusID: 2-s2.0-84888373138OAI: diva2:669198
Knowledge Foundation, 2009/0264

This work was supported in part by the KKFoundation of Sweden under Grant 2009/0264, in part by the EU Euro-pean Regional Development Fund, Mellersta Norrland, Sweden, under Grant 00156702, and in part by Länsstyrelsen Västernorrland, Sweden, under Grant 00155148.

Available from: 2013-12-03 Created: 2013-12-03 Last updated: 2014-07-24Bibliographically approved
In thesis
1. Gaining Depth: Time-of-Flight Sensor Fusion for Three-Dimensional Video Content Creation
Open this publication in new window or tab >>Gaining Depth: Time-of-Flight Sensor Fusion for Three-Dimensional Video Content Creation
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The successful revival of three-dimensional (3D) cinema has generated a great deal of interest in 3D video. However, contemporary eyewear-assisted displaying technologies are not well suited for the less restricted scenarios outside movie theaters. The next generation of 3D displays, autostereoscopic multiview displays, overcome the restrictions of traditional stereoscopic 3D and can provide an important boost for 3D television (3DTV). Then again, such displays require scene depth information in order to reduce the amount of necessary input data. Acquiring this information is quite complex and challenging, thus restricting content creators and limiting the amount of available 3D video content. Nonetheless, without broad and innovative 3D television programs, even next-generation 3DTV will lack customer appeal. Therefore simplified 3D video content generation is essential for the medium's success.

This dissertation surveys the advantages and limitations of contemporary 3D video acquisition. Based on these findings, a combination of dedicated depth sensors, so-called Time-of-Flight (ToF) cameras, and video cameras, is investigated with the aim of simplifying 3D video content generation. The concept of Time-of-Flight sensor fusion is analyzed in order to identify suitable courses of action for high quality 3D video acquisition. In order to overcome the main drawback of current Time-of-Flight technology, namely the high sensor noise and low spatial resolution, a weighted optimization approach for Time-of-Flight super-resolution is proposed. This approach incorporates video texture, measurement noise and temporal information for high quality 3D video acquisition from a single video plus Time-of-Flight camera combination. Objective evaluations show benefits with respect to state-of-the-art depth upsampling solutions. Subjective visual quality assessment confirms the objective results, with a significant increase in viewer preference by a factor of four. Furthermore, the presented super-resolution approach can be applied to other applications, such as depth video compression, providing bit rate savings of approximately 10 percent compared to competing depth upsampling solutions. The work presented in this dissertation has been published in two scientific journals and five peer-reviewed conference proceedings. 

In conclusion, Time-of-Flight sensor fusion can help to simplify 3D video content generation, consequently supporting a larger variety of available content. Thus, this dissertation provides important inputs towards broad and innovative 3D video content, hopefully contributing to the future success of next-generation 3DTV.

Place, publisher, year, edition, pages
Sundsvall: Mittuniversitetet, 2014. 228 p.
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 185
3D video, Time-of-Flight, depth map acquisition, optimization, 3DTV, ToF, upsampling, super-resolution, sensor fusion
National Category
Computer Systems
urn:nbn:se:miun:diva-21938 (URN)978-91-87557-49-1 (ISBN)
Public defence
2014-06-04, L111, Holmgatan 10, Sundsvall, 10:00 (English)
Knowledge Foundation, 2009/0264
Available from: 2014-05-16 Created: 2014-05-14 Last updated: 2014-05-16Bibliographically approved

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