A color-based approach for disparity refinement

Gupta, Raj Kumar; Cho, Siu Yeung

doi:10.1109/icarcv.2010.5707934

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Some of them refine per pixel in edges, while others refine the whole edge non-locally, therefore the edge refinement methods can be categorized into 1) local edge refinement and 2) non-local edge refinement. Local edge refinement methods (Gupta and Cho, 2010;Huang and Zhang, 2016;Lin and Liu, 2015;Park et al, 2015;Wang et al, 2013;Wu et al, 2016) adjust edge pixels in matching results to edge pixels in images. Once all the pixels are adjusted, all the edges in matching results are refined.…”

Section: Introductionmentioning

confidence: 99%

Disparity Refinement of Building Edges Using Robustly Matched Straight Lines for Stereo Matching

Huang

Qin

Chen

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Stereo dense matching has already been one of the dominant tools in 3D reconstruction of urban regions, due to its low cost and high flexibility in generating 3D points. However, the image-derived 3D points are often inaccurate around building edges, which limit its use in several vision tasks (e.g. building modelling). To generate 3D point clouds or digital surface models (DSM) with sharp boundaries, this paper integrates robustly matched lines for improving dense matching, and proposes a non-local disparity refinement of building edges through an iterative least squares plane adjustment approach. In our method, we first extract and match straight lines in images using epipolar constraints, then detect building edges from these straight lines by comparing matching results on both sides of straight lines, and finally we develop a non-local disparity refinement method through an iterative least squares plane adjustment constrained by matched straight lines to yield sharper and more accurate edges. Experiments conducted on both satellite and aerial data demonstrate that our proposed method is able to generate more accurate DSM with sharper object boundaries.</p>

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Section: Introductionmentioning

confidence: 99%

Disparity Refinement of Building Edges Using Robustly Matched Straight Lines for Stereo Matching

Huang

Qin

Chen

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Although these are robust techniques, the underestimation of the disparity map caused by weaklytextured elements occurs in some results and can compromise the accuracy of the detection of many free spaces and obstacles. To overcome these problems, some techniques apply the disparity map refinement to fit similar neighbor data in geometrical forms, like planes [7], [8]. The similar data are clusters grouped by local features (e.g., corners, edges, or colors) in the reference image.…”

Section: Introductionmentioning

confidence: 99%

A disparity map refinement to enhance weakly-textured urban environment data

Lima

Vitor

Victorino

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

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This paper presents an approach to refine noisy and sparse disparity maps from weakly-textured urban environments, enhancing their applicability in perception algorithms applied to autonomous vehicles urban navigation. Typically, the disparity maps are constructed by stereo matching techniques based on some image correlation algorithm. However, in urban environments with low texture variance elements, like asphalt pavements and shadows, the images' pixels are hard to match, which result in sparse and noisy disparity maps. In this work, the disparity map refinement will be performed by segmenting the reference image of the stereo system with a combination of filters and the Watershed transform to fit the formed clusters in planes with a RANSAC approach. The refined disparity map was processed with the KITTI flow benchmark achieving improvements in the final average error and data density.

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