A layered stereo matching algorithm using image segmentation and global visibility constraints

Bleyer, Michael; Gelautz, Margrit

doi:10.1016/j.isprsjprs.2005.02.008

Cited by 131 publications

(94 citation statements)

References 23 publications

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“…Almost all of the currently top-ranked algorithms [11,13,2,5,7,14] on this data set define a global energy function that is minimized for finding the disparities. This energy function always includes a data term and a smoothness term.…”

Section: Literature Reviewmentioning

confidence: 99%

Stereo Vision in Structured Environments by Consistent Semi-Global Matching

Hirschmüller

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)

104

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Section: Literature Reviewmentioning

confidence: 99%

Stereo Vision in Structured Environments by Consistent Semi-Global Matching

Hirschmüller

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)

104

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“…Initial matching based methods (Klaus, et al, 2006;Veldandi, et al, 2014;Bleyer and Gelautz, 2005;Guney and Geiger, 2015;Yamaguchi, et al, 2012) uses window matching or 1D label algorithms to achieve initial matching results quickly. The initial matching results are approximate to the ground truth.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…These initial matching based methods adopted SLIC (Achanta, et al, 2012) to segment the depth image into a series of patches, and still defined the stereo dense matching as a NP-hard problem. Graph cuts (Bleyer and Gelautz, 2005), belief propagation (Klaus, et al, 2006;Guney and Geiger, 2015;Yamaguchi, et al, 2012), minimum spanning tree (Veldandi, et al, 2014) were used to obtain an approximate solution iteratively.…”

Section: Review Of Related Workmentioning

confidence: 99%

Global Patch Matching

Huang

Ling

et al. 2017

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

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ABSTRACT:This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces.

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“…The high-performing methods, including Graph Cuts [35] and Belief Propagation [36][37][38], operate in two dimensions (2D). The layered [36,37,39] and Block Coordinate Descent (BCD) approaches [24] are iteratively optimized. This paper minimizes the energy function E(l) via semi-global optimization, which is inspired by SGM [25,28].…”

Section: Cost Aggregation Of Sgfmentioning

confidence: 99%

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Pang

et al. 2015

Remote Sensing

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Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second. OPEN ACCESSRemote Sens. 2015, 7 10997

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A layered stereo matching algorithm using image segmentation and global visibility constraints

Cited by 131 publications

References 23 publications

Stereo Vision in Structured Environments by Consistent Semi-Global Matching

Stereo Vision in Structured Environments by Consistent Semi-Global Matching

Global Patch Matching

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

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