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Structure guided fusion for depth map inpainting

Authors:

Fu LiAuthors Info & Claims

Pattern Recognition Letters, Volume 34, Issue 1

Pages 70 - 76

https://doi.org/10.1016/j.patrec.2012.06.003

Published: 01 January 2013 Publication History

Abstract

Depth acquisition becomes inexpensive after the revolutionary invention of Kinect. For computer vision applications, depth maps captured by Kinect require additional processing to fill up missing parts. However, conventional inpainting methods for color images cannot be applied directly to depth maps as there are not enough cues to make accurate inference about scene structures. In this paper, we propose a novel fusion based inpainting method to improve depth maps. The proposed fusion strategy integrates conventional inpainting with the recently developed non-local filtering scheme. The good balance between depth and color information guarantees an accurate inpainting result. Experimental results show the mean absolute error of the proposed method is about 20mm, which is comparable to the precision of the Kinect sensor.

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Cited By

Zhang SYu TChoi BOuyang FDing Z(2024)Radiomap Inpainting for Restricted Areas Based on Propagation Priority and Depth MapIEEE Transactions on Wireless Communications10.1109/TWC.2024.336087223:8_Part_2(9330-9344)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TWC.2024.3360872
Castellano RTerreran MGhidoni S(2023)Performance Evaluation of Depth Completion Neural Networks for Various RGB-D Camera Technologies in Indoor ScenariosAIxIA 2023 – Advances in Artificial Intelligence10.1007/978-3-031-47546-7_24(351-364)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47546-7_24
Wu YLi TShen ILin HChuang Y(2021)Multi-resolution shared representative filtering for real-time depth completionProceedings of the Conference on High-Performance Graphics10.2312/hpg.20211280(11-21)Online publication date: 6-Jul-2021
https://dl.acm.org/doi/10.2312/hpg.20211280
Show More Cited By

Structure guided fusion for depth map inpainting
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics

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Information

Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 34, Issue 1

January, 2013

107 pages

ISSN:0167-8655

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Copyright © Elsevier B.V. © 2012.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2013

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Cited By

Zhang SYu TChoi BOuyang FDing Z(2024)Radiomap Inpainting for Restricted Areas Based on Propagation Priority and Depth MapIEEE Transactions on Wireless Communications10.1109/TWC.2024.336087223:8_Part_2(9330-9344)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TWC.2024.3360872
Castellano RTerreran MGhidoni S(2023)Performance Evaluation of Depth Completion Neural Networks for Various RGB-D Camera Technologies in Indoor ScenariosAIxIA 2023 – Advances in Artificial Intelligence10.1007/978-3-031-47546-7_24(351-364)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47546-7_24
Wu YLi TShen ILin HChuang Y(2021)Multi-resolution shared representative filtering for real-time depth completionProceedings of the Conference on High-Performance Graphics10.2312/hpg.20211280(11-21)Online publication date: 6-Jul-2021
https://dl.acm.org/doi/10.2312/hpg.20211280
Shen VSpann JHarrison C(2021)FarOut Touch: Extending the Range of ad hoc Touch Sensing with Depth CamerasProceedings of the 2021 ACM Symposium on Spatial User Interaction10.1145/3485279.3485281(1-12)Online publication date: 9-Nov-2021
https://dl.acm.org/doi/10.1145/3485279.3485281
Zhang GLiu JTian LChen Y(2021)Reliably detecting humans with RGB-D camera with physical blob detector followed by learning-based filtering2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2016.7472028(2004-2008)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/ICASSP.2016.7472028
Li TNguyen NZhang XWang TSheng B(2020)PROMAR: Practical Reference Object-based Multi-user Augmented RealityIEEE INFOCOM 2020 - IEEE Conference on Computer Communications10.1109/INFOCOM41043.2020.9155506(1359-1368)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1109/INFOCOM41043.2020.9155506
Dourado APedrino E(2020)Multi-objective Cartesian Genetic Programming optimization of morphological filters in navigation systems for Visually Impaired PeopleApplied Soft Computing10.1016/j.asoc.2020.10613089:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.asoc.2020.106130
Atapour-Abarghouei AAkcay SPayen de La Garanderie GBreckon T(2019)Generative adversarial framework for depth filling via Wasserstein metric, cosine transform and domain transferPattern Recognition10.1016/j.patcog.2019.02.01091:C(232-244)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1016/j.patcog.2019.02.010
Tian YMa YQuan SXu Y(2019)Occlusion and Collision Aware Smartphone AR Using Time-of-Flight CameraAdvances in Visual Computing10.1007/978-3-030-33723-0_12(141-153)Online publication date: 7-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33723-0_12
Islam ALuboschik MJirka AStaadt O(2018)gSMOOTHProceedings of Computer Graphics International 201810.1145/3208159.3208166(175-184)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3208159.3208166
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