2SFS方法及其与立体视觉方法的集成方案综述

立体视觉（Stereo Vision）方法是目前利用图象数据获取物体三维信息的主要方法之一。但该方法在图象灰度变化较缓慢的区域，由于难以准确地进行图象间的象素配对．而严重影响了它的效果。利用从明暗重构物体三维表面形状（Shape from Shading，简称SFS）的方法与该方法相结合，是改善重构结果的主要途径之一。文章通过分析SFS问题本身的不适定性，揭示了目前几类主要的SFS算法在可靠性、稳定性、局限性以及实用性方面所存在的问题，并在此基础上，简要地介绍了四类SFS与立体视觉方法相结合的形式．说明了通过利用立体视觉为SFS补充辅助的信息来消除SFS问题的不适定性，并对过去SFs的实现方法进行有效的改进．它是提高集成系统准确性的关键。     

基于阴影的三维表面重构技术的概述

三维表面重构是计算机视觉的主要任务之一。已经发展了各种各样的重构技术,其中利用单幅图像中物体表面明暗变化来恢复其表面形状的技术尤其引人注目,这不仅因为该技术简单易于实现,更重要的是它适用于各种其它方法难以应用的场合,例如,在机载、星载、弹载环境中的利用。论文从SFS问题的研究背景出发,介绍并分析这方面的一些重要进展,通过对各种重构方法的分析与比较,提出SFS问题的研究发展趋势和一些值得研究的方向。     

基于SFS方法的三维重构及精度分析

从明暗恢复形状(SFS)是计算机视觉中三维重构问题的研究热点和难点之一,目前已有算法存在两个问题:1)选择的反射模型不符合物体表面的反射特性;2)引入的约束条件和求解过程过于复杂,求解速度慢,效率低。对SFS算法进行了详细分析,引入了朗伯特光照反射模型,对物体表面做球形假设,然后对图像做近似微分运算以求出高度函数,实现了利用单幅灰度图像恢复物体表面三维形状并仿真的数据处理方法,同时对传统线性化SFS算法和所提算法进行了实验验证,对两种模型的重构精度和算法的执行效率进行了比较和分析。实验仿真结果表明,在保证一定精度的前提下,所提算法的执行效率比传统算法高。     

Shape From Shading最小化方法分析

由明暗重构三维形状(SFS)是计算机视觉当中有关初级视觉的研究内容之一。由于该问题存在明显的不适定性,因而需要采用使问题正则化的形式,其中,将问题转变成变分形式的最小化方法是最早并得到广泛研究的方法类,但这类方法的整体表现并不好。论文对此类方法从其解决问题的思路上进行分析,找出了该类方法当中存在的一些问题,即在正则化问题的过程当中引入了无具体意义的约束条件,从而对该类算法所表现出的一些现象给出了较为合理的解释。     

从明暗恢复形状（SFS）的几类典型算法分析与评价

从明暗恢复形状（shape from shading,简称SFS）是计算机视觉中三维形状恢复问题的关键技术之一,其任务是利用单幅图象中物体表面的明暗变化来恢复其表面三维形状。为了使人们对SFC研究现状及求解SFS问题的各种算法的优缺点有个概略了解,首先介绍了求解传统SFS问题的4类方法中几个典型算法的基本原理及求解方法,并给出了实验结果,然后从算法解的唯一性、对真解的逼近程度、求解效率及适用范围等方面对这4类算法进行了比较和评价。     

多方法相融合的复杂物体深度信息的恢复

在恢复图象深度信息的方法之中,利用立体视觉的偏差来精确地定位物体的深度,是行之有效的,但只能适用于可匹配的特征点,如何建立左右图象中对应点的匹配是该方法的主要障碍;Ｓｈａｐｅ Ｆｒｏｍ Ｓｈａｄｉｎｇ方法是利用单幅图象的灰度信息获取物体表面的形状信息（表面的方向）,而不能获得其深度信息,其约束条件是表面的光滑性。在此用神经网络方法将二者融合起来,形成优势互补,用来获取物体的深度信息,通过对合成图象     

利用共轭梯度法重建三维物体表面算法的研究

通过立体视觉方法能得到一些分散点处表面信息,根据这些分散点处三维表面信息,可对三维表面进行重建,文中主要讨论利用共轭梯度法重建三维物体表面的算法。     

基于SFS方法的三维表面重建算法研究

SFS(由明暗恢复形状)方法研究是计算机三维视觉研究领域中的一个重要分支.以朗伯体光照漫反射模型为基础,对物体表面图像明暗恢复其表面高度和梯度的抽象模型进行分析,研究基于朗伯体定律求解受光点梯度的算法及SFS方法的实现原理.     

一种用于立体感增强的SFS改进算法

表面重建是计算机立体视觉中的一个重要研究领域,SFS(shape from shading)是单帧图像立体感增强的有效方法之一。为了更好地利用SFS对图像进行增强,在对朗伯体表面反射模型进行详细分析和对影响3维重建结果的关键因素进行讨论的基础上,提出了一种改进的SFS算法,并给出了具体的步骤和计算方法。实验表明,该算法的确改善了立体显示的效果。     

基于三维激光扫描的物体重构建模

物体拍摄环境具有测量数据量大、物体外轮廓信息复杂等特点,采用当前方法能够获得物体精确的三维点云数据,但缺乏颜色和纹理信息,导致物体重构精度不高,真实感较差;为此,提出一种基于三维激光扫描的物体重构建模方法;该方法通过三维激光扫描技术获取物体点云数据,采用显式的欧拉积分方法对物体整个三维曲面进行平滑,依据三角生长法进行物体三维空间三角划分,将物体网格顶点向球面进行映射,由此构造物体三角网格模型,通过迭代最近点算法对物体非同步点云数据初步匹配结果进行精确配准,利用最近点搜索算法将经多视图立体视觉算法优化后的物体颜色信息和三维点云数据坐标相融合;实验结果表明,所提方法可以快速精确地建立物体三维重构模型,验证了所提方法的可行性。     

Integrating vision modules: stereo, shading, grouping, and linelabeling

It is generally agreed that individual visual cues are fallible and often ambiguous. This has generated a lot of interest in design of integrated vision systems which are expected to give a reliable performance in practical situations. The design of such systems is challenging since each vision module works under a different and possibly conflicting set of assumptions. We have proposed and implemented a multiresolution system which integrates perceptual organization (grouping), segmentation, stereo, shape from shading, and line labeling modules. We demonstrate the efficacy of our approach using images of several different realistic scenes. The output of the integrated system is shown to be insensitive to the constraints imposed by the individual modules. The numerical accuracy of the recovered depth is assessed in case of synthetically generated data. Finally, we have qualitatively evaluated our approach by reconstructing geons from the depth data obtained from the integrated system. These results indicate that integrated vision systems are likely to produce better reconstruction of the input scene than the individual modules     

Surface descriptions from stereo and shading

Surface reconstruction from stereo and shape-from-shading have both been discussed extensively in the literature. In this paper methods that attempt to supplement stereo with analysis of shading, are reviewed, with comments on their robustness. Theoretical results on uniqueness in shape from shading are presented, and on the difficulty of estimating surface shape with local intensity operators. Preliminary experiments based on the method of Koenderinck and van Doorn, for analysing variation of shading with viewpoint, are reported. It is concluded that there is a variety of techniques for deriving qualitative shape information, which might be sufficiently robust for use in computer vision systems.     

One-eyed stereo: a general approach to modeling 3-d scene geometry

A single two-dimensional image is an ambiguous representation of the three-dimensional world?many different scenes could have produced the same image?yet the human visual system is ex-tremely successful at recovering a qualitatively correct depth model from this type of representation. Workers in the field of computational vision have devised a number of distinct schemes that attempt to emulate this human capability; these schemes are collectively known as ``shape from...' methods (e.g., shape from shading, shape from texture, or shape from contour). In this paper we contend that the distinct assumptions made in each of these schemes is tantamount to providing a second (virtual) image of the original scene, and that each of these approaches can be translated into a conventional stereo formalism. In particular, we show that it is frequently possible to structure the problem as one of recovering depth from a stereo pair consisting of the supplied perspective image (the original image) and an hypothesized orthographic image (the virtual image). We present a new algorithm of the form required to accomplish this type of stereo reconstruction task.     

Color Subspaces as Photometric Invariants

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ‘features’ that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent ‘subspaces’ of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, material-based segmentation, and motion estimation.     

Stereoscopic visualization of laparoscope image using depth information from 3D model

Laparoscopic surgery is indispensable from the current surgical procedures. It uses an endoscope system of camera and light source, and surgical instruments which pass through the small incisions on the abdomen of the patients undergoing laparoscopic surgery. Conventional laparoscope (endoscope) systems produce 2D colored video images which do not provide surgeons an actual depth perception of the scene. In this work, the problem was formulated as synthesizing a stereo image of the monocular (conventional) laparoscope image by incorporating into them the depth information from a 3D CT model. Various algorithms of the computer vision including the algorithms for the feature detection, matching and tracking in the video frames, and for the reconstruction of 3D shape from shading in the 2D laparoscope image were combined for making the system. The current method was applied to the laparoscope video at the rate of up to 5 frames per second to visualize its stereo video. A correlation was investigated between the depth maps calculated with our method with those from the shape from shading algorithm. The correlation coefficients between the depth maps were within the range of 0.70–0.95 (P < 0.05). A t-test was used for the statistical analysis.     

A real-time fuzzy hardware structure for disparity map computation

Stereo images acquired by a stereo camera setup provide depth estimation of a scene. Numerous machine vision applications deal with retrieval of 3D information. Disparity map recovery from a stereo image pair involves computationally complex algorithms. Previous methods of disparity map computation are mainly restricted to software-based techniques on general-purpose architectures, presenting relatively high execution time. In this paper, a new hardware-implemented real-time disparity map computation module is realized. This enables a hardware-based fuzzy inference system parallel-pipelined design, for the overall module, implemented on a single FPGA device with a typical operating frequency of 138 MHz. This provides accurate disparity map computation at a rate of nearly 440 frames per second, given a stereo image pair with a disparity range of 80 pixels and 640 × 480 pixels spatial resolution. The proposed method allows a fast disparity map computational module to be built, enabling a suitable module for real-time stereo vision applications.     

FPGA based disparity map computation with vergence control

Depth estimation in a scene using image pairs acquired by a stereo camera setup, is one of the important tasks of stereo vision systems. The disparity between the stereo images allows for 3D information acquisition which is indispensable in many machine vision applications. Practical stereo vision systems involve wide ranges of disparity levels. Considering that disparity map extraction of an image is a computationally demanding task, practical real-time FPGA based algorithms require increased device utilization resource usage, depending on the disparity levels operational range, which leads to significant power consumption. In this paper a new hardware-efficient real-time disparity map computation module is developed. The module constantly estimates the precisely required range of disparity levels upon a given stereo image set, maintaining this range as low as possible by verging the stereo setup cameras axes. This enables a parallel-pipelined design, for the overall module, realized on a single FPGA device of the Altera Stratix IV family. Accurate disparity maps are computed at a rate of more than 320 frames per second, for a stereo image pair of 640 × 480 pixels spatial resolution with a disparity range of 80 pixels. The presented technique provides very good processing speed at the expense of accuracy, with very good scalability in terms of disparity levels. The proposed method enables a suitable module delivering high performance in real-time stereo vision applications, where space and power are significant concerns.     

栅格法视觉传感集成及机器人实时避障

研究移动机器人在室内环境下集成双目视觉和激光测距仪信息进行障碍物实时检测。由双目视觉系统检测环境获取视差信息,通过直接对视差信息进行地平面拟合的方法快速检测障碍物;拟合过程中采用了随机采样一致性估计算法去除干扰点的影响,提高了障碍物检测的鲁棒性。用栅格地图表示基于机器人坐标系的地平面障碍物信息并对栅格信息进行提取,最后把双目视觉与激光测距得到的栅格信息进行集成。实验表明,通过传感信息集成,移动机器人既得到了充分的障碍物信息,又保证了检测的实时性、准确性。     

Recovering Shape by Shading and Stereo Under Lambertian Shading Model

A method that integrates shape from shading and stereo is reported for Lambertian objects. A rectification is proposed to convert any lighting direction from oblique to orthographic. A sparse stereo method is reported that directly uses depth information and has no foreshortening problem. The method completely solves three difficult problems in stereo, namely, recovering depth at occlusion; matching at places with similar shading and matching at smooth silhouettes. The method has been tested on both synthetic and real images. It shows superior performance compared with two recent stereo algorithms. It is also a method based on the physics of image formation. The work described in this article was fully supported by a grant from CityU (7002128).