基于先验知识的单视图三维点云重建算法研究

单幅图像的三维重建是一个不适定问题,由于图像与三维模型间存在的表示模式差异,通常存在物体自遮挡、低光照、多类对象等情况,针对目前单幅图像三维模型重建中重建模型具有歧义性的问题,提出了一种基于先验信息指导的多几何角度约束的三维点云模型重建方法。首先,通过预训练三维点云自编码器获得先验知识,并最小化输入图像特征向量与点云特征向量的差异,使得输入图像特征分布逼近点云特征分布;然后,利用可微投影模块将图像的三维点云表示形式从不同视角投影到二维平面;最后,通过最小化投影图与数据集中真实投影图的差异,优化初始重建点云。在ShapeNet和Pix3D数据集上与其他方法的定量定性比较结果表明了该方法的有效性。     

交互的极线几何建模方法

讨论了一种基于图像的交互式几何建模方法，该方法利用手动方式标定模型的平行线、特征点，得到照相机参数，进而计算出模型的几何信息，并从图像中提取纹理，提出一种最优纹理选取算法，以确保拼接纹理的合理性，该算法主要应用于构造形状规则的模型，不需要知识照相机的内外参数，简便实用，实验结果表明，该方法在一定的场合下可以满足重建要求，重建出的三维模型具有照片真实感，并且能够实时漫游。     

基于单幅图像成像一致性的组合式建筑交互建模

在组合式建筑建模中,针对单幅建筑物图像提供的信息量少、建筑物部件之间的相互遮挡等局限性,提出一种"部件建模-联合优化"的组合式建筑交互建模方法.首先根据输入的单幅建筑物图像,用户标注图像上建筑物单个部件的6个顶点,系统获取顶点坐标并利用其与三维几何体顶点之间的对应关系构建长方体初始部件模型,使得其投影与图像上的部件轮廓相匹配,以实现相机标定;然后对选定的初始部件模型进行交互式编辑以生成相邻的部件,并使得生成的相邻长方体部件投影与图像上的相应部件轮廓相匹配,基于相机内参的成像一致性完成建筑物相邻部件的重建;最后利用组合式建筑建模的投影误差和共面约束误差进行联合优化,生成精确的组合式建筑物模型,并利用建筑物纹理的对称性合成真实的三维组合式建筑.利用.NET平台下的Emgucv库和OpenGL库实现基于单幅图像的建筑物三维建模,实验结果表明,该方法能方便高效地重建单幅图像中的组合式建筑物,且具有较强真实感.     

基于网格变形的从图像重建三维人脸

从图像重建高质量三维人脸一直是计算机视觉和图形学的一个重要研究问题.不同于传统的基于立体匹配的窄基线多视几何和数据驱动的人脸形变方法,提出一种结合网格变形技术和立体视觉原理的、从图像重建高质量三维人脸模型方法.给定从不同视角拍摄的几幅人脸图像,基于健壮图像特征获得可靠的相机外部参数和稀疏三维点;在此基础上,提出一种结合几何细节保持和图像一致性约束的三维人脸变形算法重建三维人脸,通过对人脸模板的网格变形,使得变形人脸在多幅图像中的可见投影具有一致性的图像颜色强度.基于模板的人脸变形可以有效地解决三维模型成像中的遮挡问题,采用健壮估计法消除噪声、离群点和光照对目标函数收敛性的影响,对目标函数的多次非线性优化求解进一步改进了人脸重建的质量.采用合成人脸图像和真实人脸图像重建三维人脸的实验结果表明,文中算法可以从几幅宽基线图像重建高质量的三维人脸模型.     

基于中轴表达的三维模型轮廓提取方法

三维网格模型的轮廓信息在网格检索、网格简化、网格重建中有着广泛应用。现有的轮廓提取方法较为复杂,需要分析和过滤网格模型的几何特征,计算量大且有时无法生成完整的轮廓信息。近年来,三维模型的中轴表达研究趋于成熟,在表达模型几何拓扑关系上有独特的优势。因此,提出了一种基于中轴表达的三维模型轮廓提取方法：首先提取三维模型的中轴表达信息,将中轴角点投影到三维模型表面;然后根据每个区域的拓扑关系选择适合的角点连接关系,将投影点连接形成模型区域轮廓;再针对投影过程中产生的误差进行分析和纠正;最后合并区域轮廓得到三维模型的完整轮廓。通过对多个模型数据库中代表性的三维网格模型进行实验和重建误 差比较,该方法的平均重建质量较现有方法约有 10%的提升,在重建质量和轮廓信息完整度方面优于现有方法。     

基于被动立体视觉的脚型建模与比对方法

为了解决个性化鞋楦开发中三维脚型获取与检索困难的问题,提出一种三维脚型精确建模与比对方法.运用已标定的双目照相机拍摄立体图像对,进行足部整体视差估计;在对立体图像对进行自动预处理后,采用非接触式的信息采集技术,运用基于复小波的相位相关技术对足部表面进行亚像素级小区域频域匹配,考虑顺序匹配约束、连续性约束和相关性约束条件,重建足部密集三维点云信息,并自动拼接不同角度点云生成相应的足部三维模型;对其归一化后,提取其高阶矩向量进行比对,为后续的三维鞋楦设计提供必要信息.实验结果表明,该方法使足部信息采集过程快捷可靠,重建过程不需要人工干预.     

二维碰撞感知的单张图像参数化人体与服装重构

已有的基于图像的人体与服装重构大都不考虑身体与衣物之间的交互,导致独立重构结果互相穿刺.本文提出一个二维碰撞感知的从单张图像重构人体与服装的优化方法,利用SMPL和TailorNet参数化模型来分别表示人体和衣服的三维形状,进而建立约束能量求解人体形状参数,运动参数和服装细节控制参数.我们的方法在初始化阶段对输入图像进行了语义分割以及二维关节点的估计,并采用human mesh recovery初步估计人体的形状与运动参数作为能量优化的初始值.我们的能量优化由两部分构成:其一是形状与姿态约束,利用图像中人体的关节位置和着装人体区域对三维参数化模型的投影的关节位置和投影区域进行约束,保证重建模型与图像在形状与姿态上的一致性;其二是人体与衣服的碰撞约束,引入重构人体与服装模型的二维投影区域间的误差对人体与衣服进行碰撞约束,以避免相互穿刺,考虑到基于投影的约束对视点敏感,我们在三维空间中进行视点采样,从而建立多视角的二维投影约束.考虑到能量中包含了TailorNet,不容易计算梯度,我们利用爬山法交替地对人体形状,姿态和服装尺寸参数进行优化求取最优解.最后,通过一系列实验对本文方法和最近的一...     

渐开线齿轮实体建模与模板研究

基于Pro/ENGINEER的参数化设计,是指先用一组参数定义零件的三维实体模型并确定参数关系,包含几何关系和拓扑关系,然后提供给设计人员进行零件三维造型使用,设计结果的修改由尺寸驱动.设计时不必考虑零件中几何元素的准确位置或数值,而是通过向图形添加适当的约束条件定义零件形状.零件的参数化模型就是带有参数名的草绘,用户通过编辑参数,零件几何模型的拓扑信息不变,而尺寸自动改变.每一个约束形成一个代数关系式,通过约束推理确定需要修改的尺寸参数,系统将自动检索此尺寸参数对应的数据结构,找出相关参数的方程组并计算出参数,驱动参数化模型形状的改变,从而形成新的零件三维实体模型.     

锥束 X-射线 CT 投影数据的仿真

研究 CT 图像优化问题,针对提高精确性和有效性,传统的解析方法很难应用于几何形状比较复杂的物体.为了提高CT 图像重建精度,提出从离散化图像的 Radon 变换,推导出一种模拟 X-射线投影数据的模型,并提出采用模型的快速计算方法.模型不受物体内部几何形状和扫描轨迹限制,用于数字化灰度图像.利用上述方法对三维 SHEPP-LOGAN 模型的投影数据进行仿真,并对得到的数据采用 OS-EM 算法进行重建.结果表明,提高了图像精度,进一步验证了方法的有效性.     

基于图像的危化品堆垛三维几何动态建模

安全生产部门对危化品仓储堆垛五距(堆距、墙距、顶距、柱距和通道距)有严格的规定,通过对堆垛的三维动态几何建模描述其在仓库中位置的变化,可以达到动态的监管要求。提出了基于图像的堆垛三维几何动态建模,为建立像点与物点的对应关系,首先构建了五元组成像模型并提出了两点标定法,在标定好参数基础上首先求得物点坐标的解空间,再结合约束条件得到真实解空间,实现堆垛静态三维几何重建。然后对图像序列中堆垛箱体跟踪识别并三维几何重建,最终实现动态几何建模。     

基于图像序列的交互式快速建模系统

给出了一个基于图像序列的交互式三维建模系统.通过输入一段未标定的图像或视频序列,系统能够自动地恢复出摄像机参数;然后用户只需要在少量几帧图像上简单勾画出物体的形态结构,系统就能自动解析出多帧之间用户交互的对应关系,从而迅速、逼真地重建出场景的三维模型.该系统提供了点与线段的重建、直线与平面的重建、曲线与曲面的重建等功能,能够满足对现实世界中的复杂场景的快速高精度的重建要求.几组真实拍摄的图像序列的建模实验表明:该系统高效、实用.能够很好地满足实际建模需求.     

Using geometric constraints through parallelepipeds for calibration and 3D modeling

This paper concerns the incorporation of geometric information in camera calibration and 3D modeling. Using geometric constraints enables more stable results and allows us to perform tasks with fewer images. Our approach is motivated and developed within a framework of semi-automatic 3D modeling, where the user defines geometric primitives and constraints between them. In this paper, first a duality that exists between the shape parameters of a parallelepiped and the intrinsic parameters of a camera is described. Then, a factorization-based algorithm exploiting this relation is developed. Using images of parallelepipeds, it allows us to simultaneously calibrate cameras, recover shapes of parallelepipeds, and estimate the relative pose of all entities. Besides geometric constraints expressed via parallelepipeds, our approach simultaneously takes into account the usual self-calibration constraints on cameras. The proposed algorithm is completed by a study of the singular cases of the calibration method. A complete method for the reconstruction of scene primitives that are not modeled by parallelepipeds is also briefly described. The proposed methods are validated by various experiments with real and simulated data, for single-view as well as multiview cases.     

VideoPlus: a method for capturing the structure and appearance ofimmersive environments

This paper presents a simple approach to capturing the appearance and structure of immersive scenes based on the imagery acquired with an omnidirectional video camera. The scheme proceeds by combining techniques from structure-from-motion with ideas from image-based rendering. An interactive photogrammetric modeling scheme is used to recover the locations of a set of salient features in the scene (points and lines) from image measurements in a small set of keyframe images. The estimates obtained from this process are then used as a basis for estimating the position and orientation of the camera at every frame in the video clip. By augmenting the video sequence with pose information, we provide the end-user with the ability to index the video sequence spatially as opposed to temporally. This allows the user to explore the immersive scene by interactively selecting the desired viewpoint and viewing direction     

Structure recovery with multiple cameras from scaled orthographicand perspective views

This paper presents a novel framework for Euclidean structure recovery utilizing a scaled orthographic view and perspective views simultaneously. A scaled orthographic view is introduced in order to automatically obtain camera parameters such as camera positions, orientation, and focal length. Scaled orthographic properties enable all camera parameters to be calculated implicitly and perspective properties enable a Euclidean structure to be recovered. The method can recover a Euclidean structure with at least seven point correspondences across a scaled orthographic view and perspective views. Experimental results for both computed and natural images verify that the method recovers structure with sufficient accuracy to demonstrate potential utility. The proposed method can be applied to an interface for 3D modeling, recognition and tracking     

Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under various environment settings.     

基于场景几何约束未标定两视图的三维模型重建

提出了一种从两幅未标定图象重建场景三维模型的方法 .这种方法充分利用了人造结构场景中大量存在的平行性和正交性几何约束 ,即利用每幅视图中三组互相垂直的平行线 ,计算出 3个影灭点 ,从而对每幅视图进行标定 .对两幅未标定图象 ,从基本矩阵只能得到射影重构 ,如果每幅图象都已标定 ,则可将基本矩阵转化为本质矩阵 .三维重构过程有两个步骤 :先是恢复相机的位置和运动 ;后是用三角测量法计算出点的三维坐标 .对多平面组成的场景进行三维重构实验 ,所得三维模型产生新的视点图象 ,与所观察的场景一致 ,重构的两个平面夹角与实际值相近 ,实验结果表明 ,该算法是行之有效的     

A robust hybrid image-based modeling system

This paper presents a new robust image-based modeling system for creating high-quality 3D models of complex objects from a sequence of unconstrained photographs. The images can be acquired by a video camera or hand-held digital camera without the need of camera calibration. In contrast to previous methods, we integrate correspondence-based and silhouette-based approaches, which significantly enhances the reconstruction of objects with few visual features (e.g., uni-colored objects) and improves surface smoothness. Our solution uses a mesh segmentation and charting approach in order to create a low-distortion mesh parameterization suitable for objects of arbitrary genus. A high-quality texture is produced by first parameterizing the reconstructed objects using a segmentation and charting approach, projecting suitable sections of input images onto the model, and combining them using a graph-cut technique. Holes in the texture due to surface patches without projecting input images are filled using a novel exemplar-based inpainting method which exploits appearance space attributes to improve patch search, and blends patches using Poisson-guided interpolation. We analyzed the effect of different algorithm parameters, and compared our system with a laser scanning-based reconstruction and existing commercial systems. Our results indicate that our system is robust, superior to other image-based modeling techniques, and can achieve a reconstruction quality visually not discernible from that of a laser scanner.     

Self-calibration of hybrid central catadioptric and perspective cameras

Hybrid central catadioptric and perspective cameras are desired in practice, because the hybrid camera system can capture large field of view as well as high-resolution images. However, the calibration of the system is challenging due to heavy distortions in catadioptric cameras. In addition, previous calibration methods are only suitable for the camera system consisting of perspective cameras and catadioptric cameras with only parabolic mirrors, in which priors about the intrinsic parameters of perspective cameras are required. In this work, we provide a new approach to handle the problems. We show that if the hybrid camera system consists of at least two central catadioptric and one perspective cameras, both the intrinsic and extrinsic parameters of the system can be calibrated linearly without priors about intrinsic parameters of the perspective cameras, and the supported central catadioptric cameras of our method can be more generic. In this work, an approximated polynomial model is derived and used for rectification of catadioptric image. Firstly, with the epipolar geometry between the perspective and rectified catadioptric images, the distortion parameters of the polynomial model can be estimated linearly. Then a new method is proposed to estimate the intrinsic parameters of a central catadioptric camera with the parameters in the polynomial model, and hence the catadioptric cameras can be calibrated. Finally, a linear self-calibration method for the hybrid system is given with the calibrated catadioptric cameras. The main advantage of our method is that it cannot only calibrate both the intrinsic and extrinsic parameters of the hybrid camera system, but also simplify a traditional nonlinear self-calibration of perspective cameras to a linear process. Experiments show that our proposed method is robust and reliable.     

Camera model identification based on the generalized noise model in natural images

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the generalized noise model that is developed by following the image processing pipeline of the digital camera. More specifically, this model is given by starting from the heteroscedastic noise model that describes the linear relation between the expectation and variance of a RAW pixel and taking into account the non-linear effect of gamma correction. The generalized noise model characterizes more accurately a natural image in TIFF or JPEG format. The present paper is similar to our previous work that was proposed for camera model identification from RAW images based on the heteroscedastic noise model. The parameters that are specified in the generalized noise model are used as camera fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its statistical performances are theoretically established. In practice when the model parameters are unknown, two Generalized Likelihood Ratio Tests are designed to deal with this difficulty such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural JPEG images highlight the relevance of the proposed approach.     

Pan–tilt–zoom camera calibration and high-resolution mosaic generation

In this paper, we discuss the problem of estimating parameters of a calibration model for active pan–tilt–zoom cameras. The variation of the intrinsic parameters of each camera over its full range of zoom settings is estimated through a two step procedure. We first determine the intrinsic parameters at the camera’s lowest zoom setting very accurately by capturing an extended panorama. The camera intrinsics and radial distortion parameters are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the camera. Our model incorporates the variation of radial distortion with camera zoom. Both calibration phases are fully automatic and do not assume any knowledge of the scene structure. High-resolution calibrated panoramic mosaics are also computed during this process. These fully calibrated panoramas are represented as multi-resolution pyramids of cube-maps. We describe a hierarchical approach for building multiple levels of detail in panoramas, by aligning hundreds of images captured within a 1–12× zoom range. Results are shown from datasets captured from two types of pan–tilt–zoom cameras placed in an uncontrolled outdoor environment. The estimated camera intrinsics model along with the cube-maps provides a calibration reference for images captured on the fly by the active pan–tilt–zoom camera under operation making our approach promising for active camera network calibration.