一种基于4对图像对应点的欧氏重建方法

摄像机自标定算法通常是非线性的，为了得到线性的方法，提出了一种在RANSAC框架下由4对图像对应点线性标定摄像机并对场景进行鲁棒性欧氏重建的方法。当摄像机作两组平移运动时，若在两组平移运动之间摄像机具有不同的姿态，则从4对图像对应点可以线性地重建场景的欧氏几何。模拟实验和真实图像实验均证明了本文方法的可行性。     

Robust structure and motion from outlines of smooth curved surfaces

This paper addresses the problem of estimating the motion of a camera as it observes the outline (or apparent contour) of a solid bounded by a smooth surface in successive image frames. In this context, the surface points that project onto the outline of an object depend on the viewpoint and the only true correspondences between two outlines of the same object are the projections of frontier points where the viewing rays intersect in the tangent plane of the surface. In turn, the epipolar geometry is easily estimated once these correspondences have been identified. Given the apparent contours detected in an image sequence, a robust procedure based on RANSAC and a voting strategy is proposed to simultaneously estimate the camera configurations and a consistent set of frontier point projections by enforcing the redundancy of multiview epipolar geometry. The proposed approach is, in principle, applicable to orthographic, weak-perspective, and affine projection models. Experiments with nine real image sequences are presented for the orthographic projection case, including a quantitative comparison with the ground-truth data for the six data sets for which the latter information is available. Sample visual hulls have been computed from all image sequences for qualitative evaluation.     

Segment Based Camera Calibration

The basic idea of calibrating a camera system in previous approaches is to determine camera parmeters by using a set of known 3D points as calibration reference.In this paper,we present a method of camera calibration in whih camera parameters are determined by a set of 3D lines.A set of constraints is derived on camea parameters in terms of perspective line mapping.Form these constraints,the same perspective transformation matrix as that for point mapping can be computed linearly.The minimum number of calibration lines is 6.This result generalizes that of Liu,Huang and Faugeras^[12] for camera location determination in which at least 8 line correspondences are required for linear computation of camera location.Since line segments in an image can be located easily and more accurately than points,the use of lines as calibration reference tends to ease the computation in inage preprocessing and to improve calibration accuracy.Experimental results on the calibration along with stereo reconstruction are reported.     

The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences

The aim of this work is the recovery of 3D structure and camera projection matrices for each frame of an uncalibrated image sequence. In order to achieve this, correspondences are required throughout the sequence. A significant and successful mechanism for automatically establishing these correspondences is by the use of geometric constraints arising from scene rigidity. However, problems arise with such geometry guided matching if general viewpoint and general structure are assumed whilst frames in the sequence and/or scene structure do not conform to these assumptions. Such cases are termed degenerate.In this paper we describe two important cases of degeneracy and their effects on geometry guided matching. The cases are a motion degeneracy where the camera does not translate between frames, and a structure degeneracy where the viewed scene structure is planar. The effects include the loss of correspondences due to under or over fitting of geometric models estimated from image data, leading to the failure of the tracking method. These degeneracies are not a theoretical curiosity, but commonly occur in real sequences where models are statistically estimated from image points with measurement error.We investigate two strategies for tackling such degeneracies: the first uses a statistical model selection test to identify when degeneracies occur: the second uses multiple motion models to overcome the degeneracies. The strategies are evaluated on real sequences varying in motion, scene type, and length from 13 to 120 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.     

Panoramic Depth Imaging: Single Standard Camera Approach

In this paper we present a panoramic depth imaging system. The system is mosaic-based which means that we use a single rotating camera and assemble the captured images in a mosaic. Due to a setoff of the camera's optical center from the rotational center of the system we are able to capture the motion parallax effect which enables stereo reconstruction. The camera is rotating on a circular path with a step defined by the angle, equivalent to one pixel column of the captured image. The equation for depth estimation can be easily extracted from the system geometry. To find the corresponding points on a stereo pair of panoramic images the epipolar geometry needs to be determined. It can be shown that the epipolar geometry is very simple if we are doing the reconstruction based on a symmetric pair of stereo panoramic images. We get a symmetric pair of stereo panoramic images when we take symmetric pixel columns on the left and on the right side from the captured image center column. Epipolar lines of the symmetrical pair of panoramic images are image rows. The search space on the epipolar line can be additionaly constrained. The focus of the paper is mainly on the system analysis. Results of the stereo reconstruction procedure and quality evaluation of generated depth images are quite promissing. The system performs well for reconstruction of small indoor spaces. Our finall goal is to develop a system for automatic navigation of a mobile robot in a room.     

On adjustment of stereo parameters in multiview synthesis for planar 3D displays

This paper investigates the impacts of stereo parameters in multiview synthesis for planar 3D display and presents a novel parameter adjustment method for reducing visual discomforts and geometric distortions. The proposed method combines the geometry of stereoscopic capturing system with the comfortable viewing rules for the human vision system in three steps. First, an identically sized representation between the virtual space and the display space is established, and the virtual camera parameters are derived based on the stereo geometry. Second, the scale factor between the physical space and the virtual space is computed based on the depth of focus theory to diminish vergence–accommodation conflicts. Last, the capturing and displaying parameters are adjusted according to the comfortable viewing zone and personal preference. Experimental results demonstrated that a reliable basis for multiview image synthesis and a more comfortable stereo perception can be achieved by applying the proposed method.     

Least Committed Splines in 3D Modelling of Free Form Objects from Intensity Images

Generating 3D models of objects from video sequences is an important problem in many multimedia applications ranging from teleconferencing to virtual reality. In this paper, we present a method of estimating the 3D face model from a monocular image sequence, using a few standard results from the affine camera geometry literature in computer vision, and spline fitting techniques using a modified non parametric regression technique. We use the bicubic spline functions to model the depth map, given a set of observation depth maps computed from frame pairs in a video sequence. The minimal number of splines are chosen on the basis of the Schwartz's Criterion. We extend the spline fitting algorithm to hierarchical splines. Note that the camera calibration parameters and the prior knowledge of the object shape is not required by the algorithm. The system has been successfully demonstrated to extract 3D face structure of humans as well as other objects, starting from their image sequences.     

Geometrical approach for rectification of single-lens stereovision system with a triprism

This paper proposes a new method for rectification of single-lens stereovision system with a triprism. The image plane of this camera will capture three different views of the same scene behind the filter in one shot. These three sub-images can be taken as the images captured by three virtual cameras which are generated by the three Face (3F) filter (triprism). A geometry-based method is proposed to determine the pose of virtual cameras and obtain the rotational and translational transformation matrix to real camera. At the same time, the parallelogram rule and refraction rule are employed to determine the desired sketch ray functions. Followed by this, the rectification transformation matrix which applied on the images captured using the system is computed. The approach based on geometry analysis of ray sketching is significantly a simpler implementation: it does not require the usual complicated calibration process. Experimental results are presented to show the effectiveness of the approach.     

基于虚拟直线的单目视觉坐标测量模型研究

在摄像机位置随意固定和测量点共面的约束条件下,基于单目视觉提出了一种基于虚拟直线的单帧图像物点坐标测量的新算法。用几何方法代替常规代数方法,回避了传统的通过求解旋转矩阵和平移向量来获取摄像机位置和姿态参数的过程。利用直线平行、垂直的先验知识,通过空间共线四点经过射影变换的交比不变性得到目标点的位置坐标信息。实验证明：该方法简单、可行,摄像机位置随意摆放,具有很强的可操作性、实用性,且精度较高。     

Flexible three-dimensional modeling of plants using low- resolution cameras and visual odometry

The three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and three-dimensional models are publicly available.     

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。     

Tracking human motion in structured environments using adistributed-camera system

This paper presents a comprehensive framework for tracking coarse human models from sequences of synchronized monocular grayscale images in multiple camera coordinates. It demonstrates the feasibility of an end-to-end person tracking system using a unique combination of motion analysis on 3D geometry in different camera coordinates and other existing techniques in motion detection, segmentation, and pattern recognition. The system starts with tracking from a single camera view. When the system predicts that the active camera will no longer have a good view of the subject of interest, tracking will be switched to another camera which provides a better view and requires the least switching to continue tracking. The nonrigidity of the human body is addressed by matching points of the middle line of the human image, spatially and temporally, using Bayesian classification schemes. Multivariate normal distributions are employed to model class-conditional densities of the features for tracking, such as location, intensity, and geometric features. Limited degrees of occlusion are tolerated within the system. Experimental results using a prototype system are presented and the performance of the algorithm is evaluated to demonstrate its feasibility for real time applications     

3D Urban Scene Modeling Integrating Recognition and Reconstruction

Supplying realistically textured 3D city models at ground level promises to be useful for pre-visualizing upcoming traffic situations in car navigation systems. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the required maneuver will be more easily understandable. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. In this paper, we present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed. Objects in the environment, such as cars and pedestrians, may however disturb the reconstruction, as they violate the simplified geometry assumptions, leading to visually unpleasant artifacts and degrading the visual realism of the resulting 3D city model. Unfortunately, such objects are prevalent in urban scenes. We therefore extend the reconstruction framework by integrating it with an object recognition module that automatically detects cars in the input video streams and localizes them in 3D. The two components of our system are tightly integrated and benefit from each other’s continuous input. 3D reconstruction delivers geometric scene context, which greatly helps improve detection precision. The detected car locations, on the other hand, are used to instantiate virtual placeholder models which augment the visual realism of the reconstructed city model.     

摄像机线性三步定标方法研究

计算机视觉中，在对景物进行定量分析或对物体进行精确定位时，都需要进行摄像机标定，即准确确定摄像机的内部参数和外部参数。由于真实的摄像机光学模型存在很多类型的畸变，因而导致透视投影关系是非线性的。为了解决标定过程的非线性最优化问题，针对常用的带有一阶径向畸变的摄像机模型，提出了一种新的线性三步摄像机定标方法，即首先通过径向排列约束计算摄像机参数的旋转矩阵、x轴平移向量和y轴平移向量；然后根据透视投影的交比不变性解算一阶径向畸变参数；最后利用求得的摄像机参数建立有效焦距和z轴平移向量的线性方程，采用最小二乘法来得到线性解。实验表明，该方法简单快捷，不仅具有较高的标定精度，而且解决了原有算法采用非线性搜索寻优可能存在的解不稳定的问题。     

Uncalibrated reconstruction: an adaptation to structured light vision

Euclidean reconstruction from two uncalibrated stereoscopic views is achievable from the knowledge of geometrical constraints about the environment. Unfortunately, these constraints may be quite difficult to obtain. In this paper, we propose an approach based on structured lighting, which has the advantage of providing geometrical constraints independent of the scene geometry. Moreover, the use of structured light provides a unique solution to the tricky correspondence problem present in stereovision. The projection matrices are first computed by using a canonical representation, and a projective reconstruction is performed. Then, several constraints are generated from the image analysis and the projective reconstruction is upgraded into an Euclidean one—as we will demonstrate, it is assumed that the sensor behaviour is affine without loss of generality so that the constraints generation is simplified. The method provides our sensor with adaptive capabilities and permits to be used in the measurement of moving scenes such as dynamic visual inspection or mobile robot navigation. Experimental results obtained from both simulated and real data are presented.     

结合视觉惯性模组的室内三维布局鲁棒重建方法

针对使用传统单目相机的全自动三维重建方法结果精确度差和整体结构理解缺失等问题,提出一种结合视觉惯性里程计和由运动到结构的全自动室内三维布局重建系统.首先利用视觉里程计获得关键帧图像序列和对应空间位置姿态,并利用运动恢复结构算法计算精确相机位姿;然后利用多图视立体几何算法生成高质量稠密点云;最后基于曼哈顿世界假设,针对典型的现代建筑室内场景,设计一种基于规则的自底向上的布局重建方法,得到最终房间外轮廓布局.使用浙江大学CAD&CG实验室场景现场扫描数据集和人工合成的稠密点云数据集作为实验数据,在Ubuntu 16.04和PCL 1.9环境下进行实验.结果表明,文中方法对三维点云噪声容忍度高,能够有效地重建出室内场景的三维外轮廓布局.     

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.     

Visual control through the trifocal tensor for nonholonomic robots

We present a new vision-based control approach which drives autonomously a nonholonomic vehicle to a target location. The vision system is a camera fixed on the vehicle and the target location is defined by an image taken previously in that location. The control scheme is based on the trifocal tensor model, which is computed from feature correspondences in calibrated retina across three views: initial, current and target images. The contribution is a trifocal-based control law defined by an exact input–output linearization of the trifocal tensor model. The desired evolution of the system towards the target is directly defined in terms of the trifocal tensor elements by means of sinusoidal functions without needing metric or additional information from the environment. The trifocal tensor presents important advantages for visual control purposes, because it is more robust than two-view geometry as it includes the information of a third view and, contrary to the epipolar geometry, short baseline is not a problem. Simulations show the performance of the approach, which has been tested with image noise and calibration errors.