We address the problem of hole filling in RGB-D (color and depth) images, obtained from either active or stereo based sensing, for the purposes of object removal and missing depth estimation. This is performed independently on the low frequency depth information (surface shape) and the high frequency depth detail (relief) by way of a Fourier space transform and classical Butterworth high/low pass filtering. The high frequency detail is then filled using a texture synthesis method, whilst the low frequency shape information is inpainted using structural inpainting. Here, a classical non-parametric sampling approach is extended, using the concept of query expansion, to perform high frequency depth synthesis with the final output then recombined in Fourier space. In order to improve the overall depth relief (D) and edge detail accuracy, color information (RGB) is also used to constrain the sampling process within high frequency component completion. Experimental results demonstrate the efficacy of the proposed method outperforming prior work for generalized depth filling in the presence of high frequency surface relief detail.
Citation for published item: yen de v qrnderieD qr¡ egoire nd etpour erghoueiD emir nd frekonD oy F @PHIVA 9iliminting the lind spot X dpting Qh ojet detetion nd monoulr depth estimtion to QTHpnormi imgeryF9D in gomputer ision ! igg PHIV X ISth iuropen gonfereneD wunihD qermnyD eptemer VEIRD PHIVD roeedingsD rt ssF ghmX pringerD ppF VIPEVQHF veture notes in omputer sieneF @IIPIUAF Further information on publisher's website: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.Please consult the full DRO policy for further details.Abstract. Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360 • panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360 • panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt a contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.
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.Please consult the full DRO policy for further details. AbstractWe address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infrared structured light sensing. Specifically we show that fusion of disparity over these modalities prior to subsequent optimization, within the disparity space image, facilitates the recovery of scene depth information in regions where structured light sensing alone fails. This joint approach, leveraging disparity information from both structured light and cross-spectral stereo, facilitates the recovery of global scene depth comprising both texture-less object depth, where stereo sensing commonly fails, and highly reflective object depth, where structured light active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and is shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras.
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