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High-Quality Real-Time Depth-Image-Based-Rendering
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
2017 (English)In: Proceedings of SIGRAD 2017, August 17-18, 2017 Norrköping, Sweden, Linköping University Electronic Press, 2017, no 143, p. 1-8, article id 001Conference paper, Published paper (Refereed)
Abstract [en]

With depth sensors becoming more and more common, and applications with varying viewpoints (like e.g. virtual reality) becoming more and more popular, there is a growing demand for real-time depth-image-based-rendering algorithms that reach a high quality. Starting from a quality-wise top performing depth-image-based-renderer, we develop a real-time version. Despite reaching a high quality as well, the new OpenGL-based renderer decreases runtime by (at least) 2 magnitudes. This was made possible by discovering similarities between forward-based and mesh-based rendering, which enable us to remove the common parallelization bottleneck of competing memory access, and facilitated by the implementation of accurate yet fast algorithms for the different parts of the rendering pipeline. We evaluated the proposed renderer using a publicly available dataset with ground-truth depth and camera data, that contains both rapid camera movements and rotations as well as complex scenes and is therefore challenging to project accurately.

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2017. no 143, p. 1-8, article id 001
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 143
Keywords [en]
Real-Time Rendering, Depth Image, Splatting
National Category
Computer Sciences
URN: urn:nbn:se:liu:diva-162126ISBN: 978-91-7685-384-9 (print)OAI:, id: diva2:1371379
SIGRAD 2017, August 17-18, 2017 Norrköping, Sweden
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-11-19
In thesis
1. Interpolation Techniques with Applications in Video Coding
Open this publication in new window or tab >>Interpolation Techniques with Applications in Video Coding
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Recent years have seen the advent of RGB+D video (color+depth video), which enables new applications like free-viewpoint video, 3D and virtual reality. This is however achieved by adding additional data, thus increasing the bitrate. On the other hand, the added geometrical data can be used for more accurate frame prediction, thus decreasing bitrate. Modern encoders use previously decoded frames to predict other ones, meaning they only need to encode the difference. When geometrical data is available, previous frames can instead be projected to the frame that is currently predicted, thus reaching a higher accuracy and a higher compression.

In this thesis, different techniques are described and evaluated enabling such a prediction scheme based on projecting from depth-images, so called depth-image based rendering (DIBR). A DIBR method is found that maximizes image quality, in terms of minimizing the differences of the projected frame to the groundtruth of the frame it was projected to, i.e. the frame that is to be predicted. This was achieved by evaluating combinations of both state-of-the-art methods for DIBR as well as own extensions, meant to solve artifacts that were discovered during this work. Furthermore, a real-time version of this DIBR method is derived and, since the deph-maps will be compressed as well, the impact of depth-map compression on the achieved projection quality is evaluated, for different compression methods including novel extensions of existing methods. Finally, spline methods are derived for both geometrical and color interpolation.

Although all this was done with a focus on video compression, many of the presented methods are useful for other applications as well, like free-viewpoint video or animation.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 38
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1858
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:liu:diva-162116 (URN)9789179299514 (ISBN)
2019-12-09, Ada Lovelace, Campus Valla, Linköping, 13:15 (English)
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-12-10Bibliographically approved

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