Multiresolution energy minimisation framework for stereo matching

Arranz, Álvaro; Sánchez, Álvaro; Alvar, Manuel

doi:10.1049/iet-cvi.2010.0212

Cited by 8 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, this kind of approach performs efficiently with low computational complexity. Local methods contain all four steps mentioned before, preferred by some researchers in their works [13][34] [51]. For every pixel, the calculated matching cost based on various metrics is aggregated through summing up or taking the average in the window.…”

Section: Stereo Matching Algorithmsmentioning

confidence: 99%

Design of Binocular Stereo Vision System Via CNN-based Stereo Matching Algorithm

Jiao¹

2021

2021 International Conference on Networking and Network Applications (NaNA)

View full text Add to dashboard Cite

Stereo vision is one of the representative technologies in the 3D camera, using multiple cameras to perceive the depth information in the three-dimensional space. The binocular one has become the most widely applied method in stereo vision. So in our thesis, we design a binocular stereo vision system based on an adjustable narrow-baseline stereo camera, which can simultaneously capture the left and right images belonging to a stereo image pair.The camera calibration and rectification techniques are firstly performed to get rectified stereo pairs, serving as the input to the subsequent step, that is, searching the corresponding points between the left and right images. The stereo matching algorithm resolves the correspondence problem and plays a crucial part in our system, which produces disparity maps targeted at predicting the depths with the help of the triangulation principle. We focus on the first stage of this algorithm, proposing a CNN-based approach to calculating the matching cost by measuring the similarity level between two image patches. Two kinds of network architectures are presented and both of them are based on the siamese network. The fast network employs the cosine metric to compute the similarity level at a satisfactory accuracy and processing speed. While the slow network is aimed at learning a new metric, making the disparity prediction slightly more precise but at the cost of spending way more image handling time and counting on more parameters. The output of either network is regarded as the initial matching cost, followed by a series of post-processing methods, including cross-based cost aggregation as well as semi-global cost aggregation. With the trick of Winner-Take-All (WTA), the raw disparity map is attained and it will undergo further refinement procedures containing interpolation and image filtering. The above networks are trained and validated on three standard stereo datasets: Middlebury, KITTI 2012, and KITTI 2015. The contrast tests of CNN-based methods and census transformation have demonstrated that the former approach outperforms the later one on the mentioned datasets.The algorithm based on the fast network is adopted in our devised system. To evaluate the performance of a binocular stereo vision system, two types of error criteria are come up with, acquiring the proper range of working distance under diverse baseline lengths.

show abstract

Section: Stereo Matching Algorithmsmentioning

confidence: 99%

Design of Binocular Stereo Vision System Via CNN-based Stereo Matching Algorithm

Jiao¹

2021

2021 International Conference on Networking and Network Applications (NaNA)

View full text Add to dashboard Cite

show abstract

“…Local strategies incorporate every one of the four stages of the categorisation. Some of the implementation models are found in the literature by Mattoccia et al (2009), Arranz et al (2012) and Xu et al (2013). The disparity map value assignment is accomplished by optimising winner take all (WTA).…”

Section: Related Workmentioning

confidence: 99%

A performance analysis of stereo matching algorithms for stereo vision applications in smart environments

Kavitha¹,

Balakrishnan

2020

View full text Add to dashboard Cite

Stereo vision is a subfield of computer vision that tends to an essential research issue of reproducing the three-dimensional directions and focuses for depth estimation. This paper gives a relative investigation of stereo vision and matching techniques, utilised to resolve the correspondence problem. The investigation of matching algorithms is done by the use of extensive experiments on the Middlebury benchmark dataset. The tests concentrated on an examination of three stereovision techniques namely mean shift algorithm (MSA), seed growing algorithm (SGA) and multi-curve fitting (MCF) algorithm. With a specific end goal to evaluate the execution, some statistics related insights were computed. The experimental results demonstrated that best outcome is attained by the MCF algorithm in terms of depth estimation, disparity estimation and CT. The presented MCF algorithm attains a minimum computation time (CT) of 2 s whereas the other MSA and SGA require a maximum CT of 8.9 s and 7 s, respectively. The simulation results verified that the MCF algorithm reduces the processing time in a significant way than the compared methods.

show abstract

“…Local methods include all four steps of the taxonomy. Examples of implementation of such methods are provided by the work of Mattoccia et al [13], Arranz et al [14], and Xu et al [15]. The disparity map value assignment is achieved through winner take all (WTA) optimization.…”

Section: A Taxonomy For the Processing Stages Of Stereo Vision Disparmentioning

confidence: 99%

“…The assumption adopted DP is that of an ordering constraint between neighboring pixels of the same row. Recently, the multiresolution energy minimization framework introduced by Arranz et al [14] achieved real time performance while maintaining the resolution of producing disparity maps. The advantage of this framework is the reduction in computational complexity that is achieved through the multiresolution technique.…”

Section: Disparity Computation and Optimizationmentioning

confidence: 99%

Literature Survey on Stereo Vision Disparity Map Algorithms

2016

View full text Add to dashboard Cite

This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

show abstract

Multiresolution energy minimisation framework for stereo matching

Cited by 8 publications

References 27 publications

Design of Binocular Stereo Vision System Via CNN-based Stereo Matching Algorithm

Design of Binocular Stereo Vision System Via CNN-based Stereo Matching Algorithm

A performance analysis of stereo matching algorithms for stereo vision applications in smart environments

Literature Survey on Stereo Vision Disparity Map Algorithms

Contact Info

Product

Resources

About