Kinect depth restoration via energy minimization with TV&lt;inf&gt;21&lt;/inf&gt; regularization

Liu, Shaoguo; Wang, Ying; Wang, Jue; Wang, Haibo; Zhang, Jixia; Chen, Pan

doi:10.1109/icip.2013.6738149

Cited by 17 publications

(15 citation statements)

References 12 publications

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“…Jung [35] proposed a modified version of the joint trilateral filter (JTF) by using both depth and color pixels to estimate a filter kernel and by assuming the presence of no holes. Liu et al [36] employed an energy minimization method with a regularization term to fill the depth-holes and remove the noise in depth images. The linear regression model utilized was based on both depth values and pixel colors.…”

Section: Depth Image Data Smoothing and Hole-fillingmentioning

confidence: 99%

On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy c-Means Segmentation

Ouma¹

2020

Geographic Information Systems in Geospatial Intelligence

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The automated detection of pavement distress from remote sensing imagery is a promising but challenging task due to the complex structure of pavement surfaces, in addition to the intensity of non-uniformity, and the presence of artifacts and noise. Even though imaging and sensing systems such as high-resolution RGB cameras, stereovision imaging, LiDAR and terrestrial laser scanning can now be combined to collect pavement condition data, the data obtained by these sensors are expensive and require specially equipped vehicles and processing. This hinders the utilization of the potential efficiency and effectiveness of such sensor systems. This chapter presents the potentials of the use of the Kinect v2.0 RGB-D sensor, as a lowcost approach for the efficient and accurate pothole detection on asphalt pavements. By using spatial fuzzy c-means (SFCM) clustering, so as to incorporate the pothole neighborhood spatial information into the membership function for clustering, the RGB data are segmented into pothole and non-pothole objects. The results demonstrate the advantage of complementary processing of low-cost multisensor data, through channeling data streams and linking data processing according to the merits of the individual sensors, for autonomous cost-effective assessment of road-surface conditions using remote sensing technology.

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Section: Depth Image Data Smoothing and Hole-fillingmentioning

confidence: 99%

On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy c-Means Segmentation

Ouma¹

2020

Geographic Information Systems in Geospatial Intelligence

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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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“…The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. In contrast to the bilateral filtering methods [4,11], obtaining reconstruction coefficients by solving minimization problem can avoid incorrect prediction in hole filling, whereas, overemphasis on energy minimization [18,21] or total variant penalty [20] is not conducive to depth discontinuities.…”

Section: Introductionmentioning

confidence: 97%

“…Chen et al [18,19] cast the depth recovery as an energy minimization problem, which addresses the depth hole filling and denoising simultaneously. In [20], an additional total variant (TV) regularization term is introduced to produce smooth depth maps with sharp boundaries. Yang et al [21] proposed an adaptive color-guided autoregressive (AR) model for high quality depth recovery, where the depth recovery task is converted into a minimization of AR prediction errors subject to measurement consistency.…”

Section: Introductionmentioning

confidence: 99%

Trilateral constrained sparse representation for Kinect depth hole filling

Wang

et al. 2015

Pattern Recognition Letters

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Kinect depth restoration via energy minimization with TV<inf>21</inf> regularization

Cited by 17 publications

References 12 publications

On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy c-Means Segmentation

On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy c-Means Segmentation

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

Trilateral constrained sparse representation for Kinect depth hole filling

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