Range measurement from defocus gradient

Images taken of 3D objects are generally defocused. Depth-from-focus techniques use the defocus information to determine range. However, quantitative measurement of focus is difficult and requires accurate modeling of the point-spread function (PSF). We describe a new method that determines depth using the symmetry and smoothness of focus gradient with respect to the focus position. The technique is passive and uses a monocular imaging system. The performance for estimating range is experimentally demonstrated.     

立体视觉概述

立体视觉是计算机视觉领域的一个重要分支，经过了40多年的研究与发展，这门技术在许多领域发挥着越来越重要的作用。本文系统地介绍了立体视觉研究的基本原理和内容，指出双目视觉技术的实现分为图象采集、摄象机标定、特征提取、立体匹配、三维重建以及后期处理几个步骤，分析了各个步骤的技术特点、存在的问题和解决方案，分析比较了各种技术的优缺点和适用范围，并着重介绍了特征提取和立体匹配这两个步骤，简要介绍了立体视觉技术的应用范围，总结分析了目前立体视觉研究所存在的主要问题和今后的发展方向。     

立体视觉概述

立体视觉是计算机视觉领域的一个重要分支,经过了40多年的研究与发展,这门技术在许多领域发挥着越来越重要的作用。本文系统地介绍了立体视觉研究的基本原理和内容,指出双目视觉技术的实现分为图象采集、摄象机标定、特征提取、立体匹配、三维重建以及后期处理几个步骤,分析了各个步骤的技术特点、存在的问题和解决方案,分析比较了各种技术的优缺点和适用范围,并着重介绍了特征提取和立体匹配这两个步骤,简要介绍了立体视觉技术的应用范围,总结分析了目前立体视觉研究所存在的主要问题和今后的发展方向。     

摄像机参数固定的全局离焦深度恢复

利用2维离焦图像恢复景物的3维深度信息是计算机视觉中一个重要的研究方向。但是,在获取不同程度的离焦图像时,必须改变摄像机参数,例如,调节摄像机的焦距、像距或者光圈大小等。而在一些需要高倍放大观测的场合,使用的高倍精密摄像机的景深非常小,任何摄像机参数的改变都会对摄像机产生破坏性的后果,这在很大程度上限制了当前许多离焦深度恢复算法的应用范围。因此,提出了一种新的通过物距变化恢复景物全局深度信息的方法。首先,改变景物的物距获取两幅离焦程度不同的图像,然后,利用相对模糊度及热辐射方程建立模糊成像模型,最后,将景物深度信息的计算转化成一个动态优化问题并求解,获得全局景物深度信息。该方法不需改变任何摄像机参数或者计算景物的清晰图像,操作简单。仿真试验和误差分析结果表明,该方法可以实现高精度的深度信息恢复,适合应用于微纳米操作、高精度快速检测等对摄像机参数改变较为敏感的场合。     

基于图像运动的三维重建及虚拟化应用

提出了一种稳定、快速地获取摄像机视频运动图像的三维重建方法,并对该运动图像做适当的虚拟化处理以展示重建效果。采用基于尺度不变特征点匹配的摄像机标定进行三维重建。尺度不变特征对于视频图像中的特征具有优秀敏锐的匹配能力,极大地放宽了摄像机标定对于设备上的限制,拓宽了实时三维重建的适用范围。通过对系统的一系列优化,不但提升了三维重建的精度,减少了错误匹配对摄像机标定的影响,而且进一步提升了处理速度。通过在三维重建的基础之上进行虚拟化处理,展示了本系统的三维重建效果。实验结果表明,该系统适用范围广,处理速度较快,重建精度高,实现了基于视频运动图像的三维重建。     

基于shape from silhouette方法的虚拟人体构造

逼真的人体三维模型（虚拟人体）在产品设计,计算机动画,虚拟现实等领域都有着广泛的需求,为此,提出了一种基于shape from silhouette的人体三维模型构造方法,该方法所需要的器材只是一个普通的数码相机,通过从不同角度对人体进行拍摄,然后经过摄像机定标,体积生成,表面重建,纹理映射后就可重建出逼真的人体三维模型。实验结果表明,此方法精度较高,整个过程简单快捷,是一种非常实用的方法。     

Volumetric Model Reconstruction from Unrestricted Camera Views Based on the Photo-consistency of 3D Voxel Mask

This project aims to develop a three-dimensional (3D) model reconstruction system using images acquired from a mobile camera. It consists of four major steps: camera calibration, volumetric model reconstruction, surface modeling and texture mapping. A novel online scale factor estimation is developed to enhance the accuracy of the coplanar camera calibration. For the volumetric modeling, the voting-based shape-from-silhouette first generates a coarse model, which is then refined by the photo-consistency check using the novel 3D voxel mask. Our scheme can handle concave surface in a sophisticated way. Finally, the surface model is formed with the original images mapped. 3D models of some test objects are presented.     

Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.     

Automatic registration of laser reflectance and colour intensity images for 3D reconstruction

The objective of the work presented in this paper is to generate complete, high-resolution models of real world scenes from passive intensity images and active range sensors. In previous work, an automatic method has been developed in order to compute 3D models of real world scenes from laser range data. The aim of this project is to improve these existing models by fusing range and intensity data. The paper presents different techniques in order to find correspondences between the different sets of data. Based on these control points, a robust camera calibration is computed with a minimal user intervention in order to avoid the fastidious point and click phase that is still necessary in many systems. The intensity images are then re-projected into the laser coordinate frame to produce an image that combines the laser reflectance and the available video intensity images into a colour texture map.     

Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis and Argyros 2008). The proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. Three different methods have been used to qualitatively assess the improvements of the camera parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).     

基于控制点排列组合的立体视觉定标算法

针对传统的DLT算法对控制点检测精度的过度依赖问题,提出了一种基于控制点排列组合的立体视觉定标算法。该算法通过对参与计算DLT参数的控制点进行排列组合,获取经过筛选的稳定的控制点子集合,剔除发生畸变或严重偏离实际数据的控制点,从而具有较好的精度与壮健性。     

Robust refinement methods for camera calibration and 3D reconstruction from multiple images

This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13.     

双目立体摄像机标定方法研究

本文在张正友平面模板两步法的基础上,提出了一种改进的双目立体摄像机标定方法。该方法在摄像机畸变模型中充分考虑了切向畸变的影响,使得应用范围更广。本文在标定中通过选取图像中心附近的点作为初始预测值,再进行非线性搜索来确定摄像机内外参数。实验证明,本文标定模板制作简单,标定过程快捷,且具有较高的精度,完全可以满足双目立体视觉应用的要求。     

Shape Reconstruction and Camera Self-Calibration Using Cast Shadows and Scene Geometries

Recently, various techniques of shape reconstruction using cast shadows have been proposed. These techniques have the advantage that they can be applied to various scenes, including outdoor scenes, without using special devices. Previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes limit the range of application of these techniques. In this paper, we propose a method to reconstruct 3D scenes even when the camera parameters or light source positions are unknown. The technique first recovers the shape with 4-DOF indeterminacy using coplanarities obtained by cast shadows of straight edges or visible planes in a scene, and then upgrades the shape using metric constraints obtained from the geometrical constraints in the scene. In order to circumvent the need for calibrations and special devices, we propose both linear and nonlinear methods in this paper. Experiments using simulated and real images verified the effectiveness of this technique.     

Tracking-based depth recovery for virtual reality applications

This paper describes a technique for tracking-based reconstruction from camera images. The position and orientation data are provided by a commercial laser tracker rigidly attached to the camera. The tracker enables the determination of the extrinsic camera parameters. Focal length is automatically calibrated in parallel with the depth computation process by means of a Kalman filter. In addition to its simplicity, our technique enables the fast generation of three-dimensional models from two-dimensional images to be used in virtual reality applications.Received: 16 August 2003, Revised: 13 August 2004, Published online: 20 December 2004 Correspondence to: Ali Akgunduz     

排爆机器人双目立体视觉系统的研究和开发

为了辅助公安人员更好地完成排爆工作,设计和开发了一个带双目立体视觉系统的排爆机器人。该视觉系统使用Matlab7.0和机器视觉软件EVision的EasyMatch库进行开发,能实时捕获排爆机器人周围的图像信息、进行摄像机标定、图像预处理、立体图像匹配、求取可疑目标物的特征点的立体坐标,并把图像实时显示在控制台,控制排爆机器人准确地抓取可疑目标物。该机器人视觉系统成功地抓取实验,表明了它在精度上能够满足排爆机器人的项目要求。     

双目立体视觉系统的技术分析与应用前景

对双目立体视觉技术的实现过程作了分析,介绍了摄像机标定、特征点提取和立体匹配的实现方法及应用弊端,特别是给出了一种实用而有效的三维重建方法,并对双目体视技术的应用和发展做了分析与展望。     

Stereo Image Analysis of Non-Lambertian Surfaces

Stereo image analysis is based on establishing correspondences between a pair of images by determining similarity measures for potentially corresponding image parts. Such similarity criteria are only strictly valid for surfaces with Lambertian (diffuse) reflectance characteristics. Specular reflections are viewpoint dependent and may thus cause large intensity differences at corresponding image points. In the presence of specular reflections, traditional stereo approaches are often unable to establish correspondences at all, or the inferred disparity values tend to be inaccurate, or the established correspondences do not belong to the same physical surface point. The stereo image analysis framework for non-Lambertian surfaces presented in this contribution combines geometric cues with photometric and polarimetric information into an iterative scheme that allows to establish stereo correspondences in accordance with the specular reflectance behaviour and at the same time to determine the surface gradient field based on the known photometric and polarimetric reflectance properties. The described approach yields a dense 3D reconstruction of the surface which is consistent with all observed geometric and photopolarimetric data. Initially, a sparse 3D point cloud of the surface is computed by traditional blockmatching stereo. Subsequently, a dense 3D profile of the surface is determined in the coordinate system of camera 1 based on the shape from photopolarimetric reflectance and depth technique. A synthetic image of the surface is rendered in the coordinate system of camera 2 using the illumination direction and reflectance properties of the surface material. Point correspondences between the rendered image and the observed image of camera 2 are established with the blockmatching technique. This procedure yields an increased number of 3D points of higher accuracy, compared to the initial 3D point cloud. The improved 3D point cloud is used to compute a refined dense 3D surface profile. These steps are iterated until convergence of the 3D reconstruction. An experimental evaluation of our method is provided for areas of several square centimetres of forged and cast iron objects with rough surfaces displaying both diffuse and significant specular reflectance components, where traditional stereo image analysis largely fails. A comparison to independently measured ground truth data reveals that the root-mean-square error of the 3D reconstruction results is typically of the order 30–100 μm at a lateral pixel resolution of 86 μm. For two example surfaces, the number of stereo correspondences established by the specular stereo algorithm is several orders of magnitude higher than the initial number of 3D points. For one example surface, the number of stereo correspondences decreases by a factor of about two, but the 3D point cloud obtained with the specular stereo method is less noisy, contains a negligible number of outliers, and shows significantly more surface detail than the initial 3D point cloud. For poorly known reflectance parameters we observe a graceful degradation of the accuracy of 3D reconstruction.     

RBF网络在立体视觉系统中的研究

摄像机标定、立体校正以及三维表面重建是立体视觉研究的重要内容。论文充分利用RBF网络的泛函逼近以及插值能力,将其应用于以上三个方面。在摄像机标定过程,通过将标定平面放置在有效视场内的多个位置,得到一组完备的样本,经过RBF网训练后,将立体视觉的几何成像关系存储于网络中;在立体校正过程,利用极线性质,由RBF网络确定图中的一组极线,然后通过求解极值问题来确定极点位置,最后用优化方法解出校正变换矩阵;在三维重建过程,利用摄像机标定中建立的视觉模型,重建出与图像信息相一致的三维表面。与传统方法相比,该算法具有重建速度快,运算精度高,过程简易明了等优点。通过对实际的视觉系统进行实验,证明了该算法的正确性和有效性。