Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework

Chen, Chongyu; Cai, Jianfei; Zheng, Jianmin; Cham, Tat‐Jen; Shi, Guangming

doi:10.1145/2700475

Cited by 17 publications

(16 citation statements)

References 25 publications

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“…In reconstruction-based methods, missing depth values are predicted using common synthesis approaches. Since a closed-loop strategy is mostly used to resolve the recon- [82]. The energy function is made up of a fidelity term (generated depth data characteristics) and a regularization term (joint-bilateral and joint-trilateral kernels).…”

Section: Reconstruction-based Methodsmentioning

confidence: 99%

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A comparative review of plausible hole filling strategies in the context of scene depth image completion

Atapour-Abarghouei

Breckon

2018

Computers & Graphics

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.

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Section: Reconstruction-based Methodsmentioning

confidence: 99%

“…The energy function is made up of a fidelity term (generated depth data characteristics) and a regularization term (joint-bilateral and joint-trilateral kernels). Local filtering can be used instead of global filtering to make parallelization possible (reproduced from [82]). Fig.…”

Section: Reconstruction-based Methodsmentioning

confidence: 99%

A comparative review of plausible hole filling strategies in the context of scene depth image completion

Atapour-Abarghouei

Breckon

2018

Computers & Graphics

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“…Prior work in depth hole filling [7,9,12,14,23,24] is not as comprehensive as color image completion. In the depth filling literature, there have been attempts to fill color and depth via depth-assisted texture synthesis in stereo images [15], a myriad of approaches utilizing filters [13,16], temporal-based methods [17,18], reconstruction-based methods [19,20], and others [7,9,10,21]. We focus on some of the most relevant [4,7,10,21].…”

Section: Prior Workmentioning

confidence: 99%

Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion

Atapour-Abarghouei

Breckon

2018

Lecture Notes in Computer Science

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Citation for published item: etpourEerghoueiD emir nd frekonD oy F @PHIVA 9ixtended pth prioritiztion for depth (lling within onstrined exemplrEsed qfEh imge ompletionF9D in smge nlysis nd reognition X ISth snterntionl gonfereneD sgse PHIVD ¡ ovo de rzimD ortuglD tune PU!PWD PHIV Y proeedingsF D ppF QHTEQIRF veture notes in omputer sieneF @IHVVPAF Further information on publisher's website:Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.Abstract. We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color image completion, do not perform well on depth information with object boundaries obstructed or surrounded by missing regions. In the proposed method, using both color (RGB) and depth (D) information available from a common-place RGB-D image, we explicitly modify the patch prioritization term utilized for target patch ordering to facilitate improved propagation of complex texture and linear structures within depth completion. Furthermore, the query space in the source region is constrained to increase the efficiency of the approach compared to other exemplar-driven methods. Evaluations demonstrate the efficacy of the proposed method compared to other contemporary completion techniques.

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“…Whilst many seminal color image completion techniques fall short when applied to depth maps [6,16], there are specific depth filling techniques that leverage classic inpainting approaches, with or without modifications, to fill depth values [2,23,30,51]. There have also been attempts to fill a target region in one of a set of multiview photographs [4], to fill color and depth via depth-assisted texture synthesis [46], and a myriad of approaches utilizing filters [13,14,18,34,39,41], temporal-based methods [5,25,38], reconstruction-based methods [17,36,47,50], and others [2,29,35,37,40]. We focus on the most relevant to this work [29,35,40].…”

Section: Prior Workmentioning

confidence: 99%

DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation

Atapour-Abarghouei¹,

Breckon²

2017

Procedings of the British Machine Vision Conference 2017

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We address plausible hole filling in depth images in a computationally lightweight methodology that leverages recent advances in semantic scene segmentation. Firstly, we perform such segmentation over a co-registered color image, commonly available from stereo depth sources, and non-parametrically fill missing depth values based on a multipass basis within each semantically labeled scene object. Within this formulation, we identify a bounded set of explicit completion cases in a grammar inspired context that can be performed effectively and efficiently to provide highly plausible localized depth continuity via a case-specific non-parametric completion approach. Results demonstrate that this approach has complexity and efficiency comparable to conventional interpolation techniques but with accuracy analogous to contemporary depth filling approaches. Furthermore, we show it to be capable of fine depth relief completion beyond that of both contemporary approaches in the field and computationally comparable interpolation strategies. 1: l ← length of the hole. 2: c ← completion case identifier. 3: if c in {1, 2, 3, 4} then 4: i ← index of leftmost pixel in the hole 5: else if c in {5, 6, 7, 8} then 6: i ← index of rightmost pixel in the hole 7: assign initial v 0 (i) according to case c 8: assign slope according to case c 9: while i is in the hole region do 10: update v(i) according to case c 11: if c in {1, 2, 3, 4} then 12: i ← i + 1.

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Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework

Cited by 17 publications

References 25 publications

A comparative review of plausible hole filling strategies in the context of scene depth image completion

A comparative review of plausible hole filling strategies in the context of scene depth image completion

Extended Patch Prioritization for Depth Filling Within Constrained Exemplar-Based RGB-D Image Completion

DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation

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