A Thin Line Power Increase Stereo Matching Method Used in High-Precision Three-Dimensional Measurement

Human beings process stereoscopic correspondence across multiple purposes like robot navigation, automatic driving, and virtual or augmented reality. However, this bioinspiration is ignored by state-of-the-art dense stereo correspondence matching methods. Cost aggregation is one of the critical steps in the stereo matching method. In this article, we propose an optimized cross-scale cost aggregation scheme with coarse-to-fine strategy for stereo matching. This scheme implements cross-scale cost aggregation with the smoothness constraint on neighborhood cost, which essentially extends the idea of the inter-scale and intra-scale consistency constraints to increase the matching accuracy. The neighborhood costs are not only used in the intra-scale consistency to ensure that the regularized costs vary smoothly in an eight-connected neighbors region but also incorporated with inter-scale consistency to optimize the disparity estimation. Additionally, the improved method introduces an adaptive scheme in each scale with different aggregation methods. Finally, experimental results evaluated both on classic Middlebury and Middlebury 2014 data sets show that the proposed method performs better than the cross-scale cost aggregation. The whole stereo correspondence algorithm achieves competitive performance in terms of both matching accuracy and computational efficiency. An extensive comparison, including the KITTI benchmark, illustrates the better performance of the proposed method also.

Computational complexity of the local stereo matching method is affected by disparity ranges. In case of depth estimation in sequential stereo images, high computational complexity is a problem in terms of real-time processing. In this paper, we propose a temporal correlation based stereo matching method in sequential images. Using temporal information in a sequential stereo matching method provides inaccurate disparity ranges, since the estimated depth map accuracy is gradually degraded. To preserve the depth map quality in temporal stereo matching procedure, we adopt the guided image filtering for matching cost aggregation. Since the guided image filtering has a similar structure with bilateral filter, it preserves an object boundary region even in restricted disparity search ranges. Inaccurately estimated disparity values from the temporal correlation are compensated by filtering based cost aggregation method. From the experiment results, we check that the proposed depth map acquisition method preserves the depth map quality in temporal domain stereo matching.

Hardware platforms with limited processing power are often incapable of running dense stereo analysis algorithms at acceptable speed. Sparse algorithms provide an alternative but generally lack in accuracy. To overcome this predicament, we present an efficient sparse stereo analysis algorithm that applies a dense consistency check, leading to accurate matching results. We further improve matching accuracy by introducing a new feature detector based on FAST, which exhibits a less clustered feature distribution. The new feature detector leads to a superior performance of our stereo analysis algorithm. Performance evaluation shows that the proposed stereo matching system achieves processing rates above 200 frames per second on a commodity dual core CPU, and faster than video frame-rate processing on a low-performance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms.

An asymmetric post-processing for correspondence problem

Stereo matching is an essential basis for various applications, but most stereo matching methods have poor generalization performance and require a fixed disparity search range. Moreover, current stereo matching methods focus on the scenes that only have positive disparities, but ignore the scenes that contain both positive and negative disparities, such as 3D movies. In this paper, we present a new stereo matching pipeline that first computes semi-dense disparity maps based on binocular disparity, and then completes the rest depending on monocular cues. The new stereo matching pipeline have the following advantages: It 1) has better generalization performance than most of the current stereo matching methods; 2) relaxes the limitation of a fixed disparity search range; 3) can handle the scenes that involve both positive and negative disparities, which has more potential applications, such as view synthesis in 3D multimedia and VR/AR. Experimental results demonstrate the effectiveness of our new stereo matching pipeline.

The stereo matching algorithm is a technique that analyses two or more images captured at diverse view points in order to find positions in real 3D space for the pixels of 2D image. The stereo matching methods have been used in various fields such as drawing the topographical map from aerial photograph and finding the depth information of objects in machine vision system. Nowdays, optical motion capture techniques using stereo matching algorithms are being developed for visual applications such as virtual reality or 3D graphics. Stereo matching algorithms can be generally classified into two methods: feature-based and area-based ones. The feature-based method matches the feature elements between two images, and uses the interpolation to obtain the disparity information for the pixels other than the feature elements, while the area-based method performs matching between pixels in two images by calculating the correlations of the pixels residing in the search window. Area-based method cannot match feature element with more accuracy than feature-based method even though it can make more dense disparity map. Moreover, it has more possibility of error in the area of insufficient texture information or depth discontinuities. In this paper we present a novel technique for area-based stereo matching algorithm which provides more accurate and error-prone matching capabilities by using adaptive search range and window size. We propose two new strategies (1) for determining search range adaptively from the disparity map and multiresolutional images of region segments obtained by applying feature-based algorithm, and (2) for changing the window size adaptively according to the edge information derived from the wavelet transform such that the combination of two adaptive methods in search range and window size greatly enhance accuracy while reducing errors. We test our matching algorithms for various types of images, and shall show the outperformance of our stereo matching algorithm.

A method of stereo matching which is based on color image segmentation and cross adaptive window has been used to solve the problems of matching imprecision in depth discontinuity regions and low texture regions. This article first produces the matching cost based on color segmentation region by segmenting color image and the one based on cross adaptive window which makes use of color similarity, and then integrates the two to form the combined matching cost. At last it uses the fast searching method of optical parallax to narrow the matching range and then raise the efficiency rate. The result of this experiment can improve the matching accuracy in depth discontinuity regions and low texture regions and raise the matching speed.

This paper presents a stereo image coding method. We first use a new stereo matching method with difference-based adaptive searching windows to get the fine disparity. Then, we encode the disparity instead of one channel of the stereo image while the other channel is encoded normally. The stereo matching employs the continuity of difference of stereo images to compute the size of adaptive searching windows, which is superior to the approaches based on fixed searching windows. Experimental results show that the proposed scheme can provide better performance for stereo image coding.

Stereo matching is a branch of 3D vision and has a wide range of applications in 3D reconstructionand autonomous driving. Recently, stereo matching methods leverage all the information of the stereo image to calculate disparity map. However, these methods still have difficulties in texture-less areas and occlusion areas, and post-processing improves accuracy. Therefore, there is a high computational cost in feature extraction and post-processing. In this paper, we propose a stereo matching method based on features of image patches to predict the disparity of non-occlusion areas instead of full image features. And aggregation methods are performed to modify all kinds of mismatching pixels based on the correct disparity in the non-occlusion areas. Furthermore, we evaluated the proposed method on the Middlebury dataset. The result shows that the proposed method performs well in all areas.

Three-dimensional profile measurement of the blade based on surface structured light

This paper presents a novel method for stereo matching with occlusion handling. In order to estimate optimal cost, we define an energy function and solve the iterative equation with the numerical method. We improve performance and convergence rate by using several acceleration techniques. The proposed method is computationally efficient since it does not use color segmentation or any global optimization techniques. For occlusion handling, which has not been performed effectively by any conventional cost aggregation approaches, we combine the occlusion problem with the proposed minimization scheme. Asymmetric information is used so that few additional computational loads are necessary. Experimental results show that performance is comparable to that of many state-of-the-art methods.

In order to realize the precision machining and assembly of the parts, the geometrical dimensions of the surface of the local assembly surfaces need to be strictly guaranteed. In this paper, a local high-precision three-dimensional measurement method based on line laser measuring instrument is proposed to achieve a high degree of accuracy of the three-dimensional reconstruction of the surface. Aiming at the problem of two-dimensional line laser measuring instrument which lacks one-dimensional high-precision information, a local three-dimensional profile measuring system based on an accurate single-axis controller is proposed. First of all, a three-dimensional data compensation method based on spatial multi-angle line laser measuring instrument is proposed to achieve the high-precision measurement of the default axis. Through the pretreatment of the 3D point cloud information, the measurement points can be restored accurately. Finally, the target spherical surface is needed to make local three-dimensional scanning measurements for accuracy verification. The experimental results show that this scheme can get the local three-dimensional information of the target quickly and accurately, and achieves the purpose of gaining the information and compensating the error for laser scanner information, and improves the local measurement accuracy.

Designing a model to quickly obtain an accurate matching cost is a vital problem in the stereo matching method. We present an algorithm called MC-HDCNN, which is based on hybrid dilated convolution neural network, for computing matching cost of two image patches. HDCNN uses the dilated convolution of the series to obtain a larger receptive field, while avoiding the “gridding” effect and ensuring the integrity of the receptive field. In addition, by adding batch normalization layer after each layer of the convolution, the gradient dispersion in the backward propagation and the generalization of the network can be improved effectively. We evaluate our method on the KITTI stereo data set. The results show that the proposed algorithm has certain advantages in accuracy and speed.

Fractal generated turbulence is highly anisotropy and inhomogeneous, particularly, within its unique turbulence production regime. This study aims to develop an experimental-based detection method which allows the details of fluid-structure-interaction (FSI) via grid-net interplays to better express the strength of fractal grid induced flow dynamics. Hence, a novel approach is initiated to perform nets photogrammetry for 3D-reconstruction through an innovative means that permits the historically difficult task of reconstructing nets to be achieved. A keypoint extraction algorithm designed specifically for nets dubbed Neighborhood OCCupancy (NOCC), and a complementary stereo matching algorithm named Propagative Stereo Matching (PSM) are presented. The former identifies the center of intersections in the net by determining the percentage of an ellipse filled at each pixel. PSM works on the NOCC keypoints via matching pairs close to a known match and using the newly matched pairs as reference for subsequent matches in a way reminiscence to how humans would perform the stereo matching task manually. The effectiveness, accuracy and robustness of the detection are carefully validated using three different net geometries, which are individually subjected to three synthetic configurations. Consequently, the system repeatability, utilisation, stereo matching accuracy and reconstruction accuracy, of which, with the mean of all cases outstandingly deduced to 99.0%, 87.0%, 100% and 0.12mm, respectively. The presented method is then employed to elucidate the windward flow straightener influence on grid-net fluid flow interplays. Overall, the current framework may be an enabling factor in providing critical FSI-features to realize the mechanical strength of fractal generated turbulence.

To fulfill the applications on robot vision, the commonly used stereo matching method for depth estimation is supposed to be efficient in terms of running speed and disparity accuracy. Based on this requirement, Delaunay-based stereo matching method is proposed to achieve the aforementioned standards in this paper. First, a Canny edge operator is used to detect the edge points of an image as supporting points. Those points are then processed using a Delaunay triangulation algorithm to divide the whole image into a series of linked triangular facets. A proposed module composed of these facets performs a rude estimation of image disparity. According to the triangular property of shared vertices, the estimated disparity is then refined to generate the disparity map. The method is tested on Middlebury stereo pairs. The running time of the proposed method is about 1 s and the matching accuracy is 93%. Experimental results show that the proposed method improves both running speed and disparity accuracy, which forms a steady foundation and good application prospect for a robot’s path planning system with stereo camera devices.