Sparse view stereo matching

This paper addresses the sparse view matching problem where the camera parameters lie within ranges depending on the sensor used. An approach, based on homographic transformation, is proposed. The operation is split into two phases. The first phase deals with matches on the ground surface, which is considered planar. The second phase detects matches on arbitrary planes. This is done by detecting junctions of different shapes and reconstructing planes inferred by them. Two versions of that approach are suggested based on the sum of absolute differences and the variance normalized correlation techniques. The first technique is computationally inexpensive while the later is more robust to changes in lighting condition between views. Experiments show that our approach outperforms non-homographic correlation.     

彩色图像对自适应匹配算法

提出了两种方法来提高图像匹配的精度。一种方法是改变自适应匹配窗口的选择方法,与原来的矩形窗口相比较,提出的窗口选取方法能在低纹理区域得到包含有效信息量更多、更接近实际边界的窗口。此外,通过降低边界点相似度在计算过程中的权重来降低边界点影响力,使得视差不连续区域的匹配精度得到提高。算法采用了Middlebury网站上提供的四幅立体图像对Tsukuba、Venus、Teddy和Cones分别进行实验验证。实验表明文中提出的两种方法对四幅图像的匹配精度均有所提高。     

基于能量的图像立体匹配算法

通过引入能量函数，给出了一种快速高精度求取匹配点算法——基于能量的算法。首先快速求取初始匹配点，然后对初始匹配点的相邻区域的点进行能量估计，计算其判断函数值，再根据判断函数值对原始数据进行矫正，得到精确的匹配点数据，从而实现匹配点的快速高精度估计。     

基于区域的立体匹配优化方法

为解决立体匹配过程中存在大量重复计算问题,提出了灰度差平方和(SSD)算法中模板计算的优化方法.该方法通过分析相邻像素计算模板的平移特性及其内部的相关性,提出将像素点计算模板的结果采用列存储方式,并在匹配过程中采用循环队列数据结构的存储策略.根据待匹配像素点在图像中所处位置的情况,分别给出了利用已计算模板的相关列,实现...     

快速鲁棒的立体匹配方法

针对全局匹配算法复杂度太高的问题,提出了一种基于图割的快速鲁棒立体匹配算法。一方面,参考局部立体匹配算法的思想,优化图割求解中的网格图的节点和边缘个数,使得在进行图割全局优化求解时不需要遍历所有视差值,从而减少匹配时间;另一方面,用二维线性插值算法计算亚像素,提高匹配精度和算法鲁棒性。实验结果表明,该算法快速、鲁棒、准确。     

基于改进Census变换的立体匹配算法

针对基于传统Census变换的立体匹配方法精度不高的问题, 提出一种基于坐标模板Census变换的立体匹配方法。该方法首先设计一个坐标为高斯分布的变换模板指导Census变换; 其次, 以Census变换结果之间的Hamming距离作为匹配代价求取初始视差, 同时使用SSE4. 2指令提高Hamming距离计算效率; 再次, 使用非局部代价聚合方法优化视差结果; 最后, 获得精度较高的视差图像。实验结果表明, 该算法具有较好的匹配精度和较高的匹配效率。     

Acyclic belief propagation for stereo matching

Most of the works for solving early vision problems, such as stereo, rely on inference techniques and cyclic graphical models. Although Loopy Belief Propagation is one of the most interesting tools as it applies to that research field, loops are still a major challenge to face: the main problem arising with them is the introduction of approximation issues which could affect the accuracy of the final results. Therefore, breaking loops could be the winning strategy to overcome these problems. In this paper, we describe a way to consider an MRF grid as composed by a suitable set of singly connected graphs. Then, an acyclic propagation scheme, based on that set, is given accordingly. Experiments performed on different stereo pairs will prove that our approach is up to the current state-of-art and it spreads several new research issues for the future.     

双目立体匹配算法的研究与进展

立体匹配一直是计算机视觉领域的一个中心研究问题.首先综合介绍了立体匹配算法的研究概况,论述了双目立体匹配算法中各种约束的核心概念和适用范围;然后重点归纳分析了立体匹配算法的分类及其发展过程中的各种演化算法,对其关键技术进行了剖析和比较,并总结了目前存在的主要难题和可能的解决途径;最后对该领域存在的问题和技术发展趋势进行了分析和讨论.     

一种快速立体视觉边缘匹配算法

提出了一种立体视觉边缘匹配快速算法。通过小波变换,得到了图像的边缘和边缘幅角 并定义了边缘幅角约束。由视差梯度的分布密度函数,导出了左图像连续边缘上相邻两点在右图像 中的对应点的坐标间的相互约束关系,从而限定了右图像中匹配点的搜索范围。最后给出了基于视 差梯度约束和边缘幅角约束的快速边缘匹配算法。     

Stochastic analysis of stereo quantization error

The probability density function of the range estimation error and the expected value of the range error magnitude are derived in terms of the various design parameters of a stereo imaging system. In addition, the relative range error is proposed as a better way of quantifying the range resolution of a stereo imaging system than the percent range error when the depths in the scene lie within a narrow range     

Local algorithms on dense two-frame stereo matching

Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps: cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.     

基于色彩相似度的自适应立体匹配

提出了一种结合权值矩阵和相似性系数矩阵构造的区域匹配方法。该方法首先运用色彩相似性和距离临近性对窗内的每一点相对于待匹配点的自适应权值进行分配,得到一个权值矩阵,为了提高在视差不连续区域的匹配精度,使用了边界点矩阵来降低相对应点的权值。然后在RGB色彩空间中根据待匹配点和对应点的匹配窗内的每一点的颜色绝对差值和来自适应分配相似性系数矩阵。最后利用上述方法对Middlebury网站上提供的四幅立体图像对Tsukuba、Venus、Teddy和Cones进行了实验,总体正确率分别达到了91.82%、96.19%、76.6%和86.9%。     

Homography-guided stereo matching for wide-baseline image interpolation

Computational Visual Media - Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open...     

Near real-time stereo matching using geodesic diffusion

Adaptive-weight algorithms currently represent the state of the art in local stereo matching. However, due to their computational requirements, these types of solutions are not suitable for real-time implementation. Here, we present a novel aggregation method inspired by the anisotropic diffusion technique used in image filtering. The proposed aggregation algorithm produces results similar to adaptive-weight solutions while reducing the computational requirements. Moreover, near real-time performance is demonstrated with a GPU implementation of the algorithm.     

Fast stereo matching using adaptive guided filtering

Dense disparity map is required by many great 3D applications. In this paper, a novel stereo matching algorithm is presented. The main contributions of this work are three-fold. Firstly, a new cost-volume filtering method is proposed. A novel concept named “two-level local adaptation” is introduced to guide the proposed filtering approach. Secondly, a novel post-processing method is proposed to handle both occlusions and textureless regions. Thirdly, a parallel algorithm is proposed to efficiently calculate an integral image on GPU, and it accelerates the whole cost-volume filtering process. The overall stereo matching algorithm generates the state-of-the-art results. At the time of submission, it ranks the 10th among about 152 algorithms on the Middlebury stereo evaluation benchmark, and takes the 1st place in all local methods. By implementing the entire algorithm on the NVIDIA Tesla C2050 GPU, it can achieve over 30 million disparity estimates per second (MDE/s).     

双目视觉的匹配算法综述

双目视觉是获取对现实世界立体感知的重要方法,在自动驾驶等领域得到了普遍 的应用。立体匹配是实现双目感知的前提,该算法对左右摄像机拍摄的照片进行像素级的匹配, 生成稠密视差图,从而获取了三维坐标信息。概述了立体匹配算法近 20 年来的发展过程,围绕 基于人工特征和深度学习两个方向进行了综述,对算法实现过程中的代价计算、代价聚合、视 差计算和视差求精进行分析讨论,评估了算法的准确性和时间复杂度。最后总结了立体匹配算 法面对的挑战和对未来发展的展望。     

Hybrid cost aggregation for dense stereo matching

Multimedia Tools and Applications - Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be...     

Learning and feature selection in stereo matching

We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we develop an adaptive method for refining the feature set. This adaptive method analyzes the feature error to locate areas of the image that would lead to false matches. Then these areas are used to guide the search through feature space towards maximizing the class separation distance between the correct match and the false matches. Third, we introduce a self-diagnostic method for determining when apriori knowledge is necessary for finding the correct match. If the a priori knowledge is necessary then we use a surface reconstruction model to discriminate between match possibilities. Our algorithm is comprehensively tested against fixed feature set algorithms and against a traditional pyramid algorithm. Finally, we present and discuss extensive empirical results of our algorithm based on a large set of real images     

Oriented-linear-tree based cost aggregation for stereo matching

Multimedia Tools and Applications - Matching cost aggregation is one of the most important steps in dense stereo correspondence, and non-local cost aggregation methods based on tree structures have...     

基于NCC的改进立体匹配算法

在双目立体视觉系统中,图像匹配是关键步骤之一。在众多匹配算法中,归一化互相关(NCC)算法由于具有精度高、鲁棒性强等优点得到广泛应用,但其计算量大、运算速度较慢,使其难以在线应用。为此,本文提出一种改进的NCC立体匹配算法,通过引入积分图像和平方积分图像,将矩形窗口区域像素求和运算转化为四个像素点值的简单相加减,同时剔除基准图像中无法匹配区域以减小搜索范围,使计算复杂度得到简化,计算量大为降低。实验证明,改进后的NCC算法在保证匹配质量的基础上,执行速度得到显著提高,利于在线应用。