Matching cost computation based on sum of absolute RGB differences

2014 ◽

Vol XL-5 ◽

pp. 187-194 ◽

Cited By ~ 19

Author(s):

E. Dall'Asta ◽

R. Roncella

Keyword(s):

Computer Vision ◽

Stereo Matching ◽

Least Square ◽

Stereo Image ◽

Dense Matching ◽

Consistency Constraints ◽

Global Matching ◽

Matching Cost ◽

Software Codes ◽

Matching Techniques

Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision.<br><br> The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed.<br><br> The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.

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Globally Consistent 3D LiDAR Mapping with GPU-accelerated GICP Matching Cost Factors

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3113043 ◽

2021 ◽

pp. 1-1

Author(s):

Kenji Koide ◽

Masashi Yokozuka ◽

Shuji Oishi ◽

Atsuhiko Banno

Keyword(s):

Matching Cost ◽

3D Lidar ◽

Cost Factors

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TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-3-59-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 59-66 ◽

Cited By ~ 3

Author(s):

Han Hu ◽

Chongtai Chen ◽

Bo Wu ◽

Xiaoxia Yang ◽

Qing Zhu ◽

...

Keyword(s):

Image Matching ◽

Urban Areas ◽

Point Clouds ◽

Parameter Tuning ◽

Census Transform ◽

Urban Scenes ◽

Dense Image ◽

Global Matching ◽

Matching Cost ◽

Sharp Edges

Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.

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