用单数码相机实现物体表面的三维重建

根据由运动重建物体结构的原理,设计了一个简便易操作的三维重建系统,具体做法是:先用张氏标定法求得内参数矩阵,然后在两个不同的未知位置拍摄物体得到两幅图像,经立体匹配后,利用图像特征点的对应关系求解基本矩阵和本质矩阵,分解本质矩阵获得两个拍摄位置确定的摄像机运动参数(旋转矩阵和平移向量),进而求出相机在两个位置的投影矩阵,最后用三角法计算出物体表面特征点的三维坐标并在OpenGL中重建物体表面.和传统的立体视觉系统相比,本系统只需要一台数码相机和平面方格模板就可以实现三维重建,因此适用于普通相机用户.     

由单幅二维灰度图像重构物体表面形状*

鉴于目前很少有论文讨论完整的由单幅二维灰度图像重构物体表面形状的算法,包括它的控制参数的估计及算法的实现,介绍了一种完整的SFS算法.它在考虑自遮掩影响的情况下,有效地估计了SFS算法中涉及的各种控制参数,并引入亮度约束、灰度梯度约束和可积性约束,计算出表面高度和表面向量,实现三维重构.与传统的算法相比,本算法无论是在速度还是在精度方面都达到了比较高的水平,具有一定的应用前景.最后还指出了在MATLAB中实现需要注意的问题.     

单幅图像三维表面重建的算法研究与实现

以单幅二维图像为研究对象,应用SFS的基本理论,首先对二维图像进行了光照的倾角和偏角的估计,在光源坐标系下计算出物体每一点的表面梯度;之后,旋转坐标系,在观察坐标系下得到物体的表面梯度;最后采用三点辛普森公式求出物体表面的高度。整个算法流程简单,求解层次分明,重构满意度较高。     

3D-CDRNet: Retrieval-based dense point cloud reconstruction from a single image under complex background

3D reconstruction technique based on deep learning is gaining increasing attention from researchers. The majority of current 3D reconstruction techniques require a simple background, which limit their applications on complex background image. Extracting point cloud features comprehensively is also extremely difficult. This paper design a novel 3D reconstruction network to overcome these limitations. Firstly, we get the image and the retrieved point cloud that is the most similar to the input image. Secondly, to learn the features of the retrieved point cloud, the network encodes and decodes the single image and the retrieved point cloud to generate sparse point cloud. Finally, the proposed dense module densifies the sparse point cloud into the dense point cloud. We use single image of complex background and public dataset to evaluate our network. The reconstruction results indicate that the network surpasses previous reconstruction networks.     

基于单张人脸图片和一般模型的三维重建方法

提出一种使用单幅人脸照片进行特征提取、标准模型变形的全自动三维人脸重建方法。使用改进ASM方法自动精确提取人脸特征点,通过使稀疏形变模型匹配平面特征点来获取照片人脸的深度信息,再将一般人脸模型变形到特定人脸。基于肤色模型优化的ASM提取人脸特征,使得一定角度的侧面照片也可以有很好的重建效果。同时,使用基于肤色模型的纹理融合技术使侧面信息缺失的问题得到很好解决。实验证明,该方法快速简便,只用单幅照片全自动化完成重建,无须用户交互,生成的三维模型有较好的真实感。     

由航空影像自动重建大范围3维地形

目的 针对由航空影像自动生成大范围3维地形的立体模型配准问题,提出一种自动配准全部立体模型的方法,从而生成大范围3维地形。方法 首先由相邻影像构建独立的立体模型;然后根据特征匹配同名点在公共影像上的坐标对应关系,自动提取相邻模型的连接点;通过循环遍历搜索,自动配准全部立体模型,进而构建全航摄区的大范围3维地形。结果 采用两组数据进行实验,结果显示,两组数据全部3维模型的均方配准误差分别为5.20像素和2.63像素。本文方法生成的大范围地形的相对精度较高;对第2组数据的结果采用控制点进行绝对定向,并用检查点进行精度评估,结果显示全部检查点的均方平面和高程误差分别为0.326 m和0.502 m,生成的大范围地形达到了较高的绝对精度。结论 本文方法可自动化执行,仅需输入一系列有一定重叠的航空影像,即可自动生成按一定方式组织的大范围3维地形产品。该方法生成的大范围地形既可用于3维场景浏览,也可用于地形量测,但不适用于由激光扫描获取的点云数据的配准。     

Single-view-based 3D facial reconstruction method robust against pose variations

The 3D Morphable Model (3DMM) and the Structure from Motion (SfM) methods are widely used for 3D facial reconstruction from 2D single-view or multiple-view images. However, model-based methods suffer from disadvantages such as high computational costs and vulnerability to local minima and head pose variations. The SfM-based methods require multiple facial images in various poses. To overcome these disadvantages, we propose a single-view-based 3D facial reconstruction method that is person-specific and robust to pose variations. Our proposed method combines the simplified 3DMM and the SfM methods. First, 2D initial frontal Facial Feature Points (FFPs) are estimated from a preliminary 3D facial image that is reconstructed by the simplified 3DMM. Second, a bilateral symmetric facial image and its corresponding FFPs are obtained from the original side-view image and corresponding FFPs by using the mirroring technique. Finally, a more accurate the 3D facial shape is reconstructed by the SfM using the frontal, original, and bilateral symmetric FFPs. We evaluated the proposed method using facial images in 35 different poses. The reconstructed facial images and the ground-truth 3D facial shapes obtained from the scanner were compared. The proposed method proved more robust to pose variations than 3DMM. The average 3D Root Mean Square Error (RMSE) between the reconstructed and ground-truth 3D faces was less than 2.6 mm when 2D FFPs were manually annotated, and less than 3.5 mm when automatically annotated.     

Using vanishing points for camera calibration and coarse 3D reconstruction from a single image

In this paper, we show how to calibrate a camera and to recover the geometry and the photometry (textures) of objects from a single image. The aim of this work is to make it possible walkthrough and augment reality in a 3D model reconstructed from a single image. The calibration step does not need any calibration target and makes only four assumptions: (1) the single image contains at least two vanishing points, (2) the length (in 3D space) of one line segment (for determining the translation vector) in the image is known, (3) the principle point is the center of the image, and (4) the aspect ratio is fixed by the user. Each vanishing point is determined from a set of parallel lines. These vanishing points help determine a 3D world coordinate system R _o. After having computed the focal length, the rotation matrix and the translation vector are evaluated in turn for describing the rigid motion between R _o and the camera coordinate system R _c. Next, the reconstruction step consists in placing, rotating, scaling, and translating a rectangular 3D box that must fit at best with the potential objects within the scene as seen through the single image. With each face of a rectangular box, a texture that may contain holes due to invisible parts of certain objects is assigned. We show how the textures are extracted and how these holes are located and filled. Our method has been applied to various real images (pictures scanned from books, photographs) and synthetic images.     

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.     

Dynamic 3D surface reconstruction and motion modeling from a pan–tilt–zoom camera

3D surface reconstruction and motion modeling has been integrated in several industrial applications. Using a pan–tilt–zoom (PTZ) camera, we present an efficient method called dynamic 3D reconstruction (D3DR) for recovering the 3D motion and structure of a freely moving target. The proposed method estimates the PTZ measurements to keep the target in the center of the field of view (FoV) of the camera with the same size. Feature extraction and tracking approach are used in the imaging framework to estimate the target's translation, position, and distance. A selection strategy is used to select keyframes that show significant changes in target movement and directly update the recovered 3D information. The proposed D3DR method is designed to work in a real-time environment, not requiring all frames captured to be used to update the recovered 3D motion and structure of the target. Using fewer frames minimizes the time and space complexity required. Experimental results conducted on real-time video streams using different targets to prove the efficiency of the proposed method. The proposed D3DR has been compared to existing offline and online 3D reconstruction methods, showing that it uses less execution time than the offline method and uses an average of 49.6% of the total number of frames captured.     

深度学习在单图像三维模型重建的应用

针对基于单图像重建的三维模型具有高度不确定性问题,提出了一种基于深度图像估计、球面投影映射、三维对抗生成网络相结合的网络模型算法。首先,通过深度估计器得到输入图像的深度图像,这有利于对图像进一步的分析;其次,将得到的深度图像通过球面投影映射转换为三维模型;最后,利用三维对抗生成网络对重建的三维模型的真实性进行判断,建立更逼真的三维模型。理论分析和仿真实验表明,与学习先验知识生成三维模型的算法LVP相比,所提模型在真实三维模型与重建三维模型的交并比（IoU）上提高了20.1%,倒角距离（CD）缩小了13.2%。实验结果表明,所提模型在单视图三维模型重建中具有良好的泛化能力。     

A camera planning method for the 3D reconstruction of a single object based on statistical deformation model

In order to provide sufficient information for the 3D reconstruction of a single, static and known type object, this paper proposes measure indexes to evaluate the information amount and a camera planning method to guide the search of the optimal camera positions. The planning method is based on Statistical Deformation Model (SDM) which is generated from Point Distribution Model (PDM) by the Principal Component Analysis (PCA) method. The position from which the individual factor coefficients are observed clearly and the principal shape features are identified distinctly is the best viewpoint. The observed times of landmark are used to exclude the redundancy among views. So, in an iteration process new SDM is repeatedly computed to pick out the optimal viewpoints until all the landmarks are observed sufficiently. Therefore abundant information with the minimum redundancy is provided by fewer cameras, and an experiment which layout cameras for foot reconstruction was demonstrated.     

Automatic reconstruction of 3D human arm motion from a monocular image sequence

A model-based approach to reconstruction of 3D human arm motion from a monocular image sequence taken under orthographic projection is presented. The reconstruction is divided into two stages. First, a 2D shape model is used to track the arm silhouettes and second-order curves are used to model the arm based on an iteratively reweighted least square method. As a result, 2D stick figures are extracted. In the second stage, the stick figures are backprojected into the scene. 3D postures are reconstructed using the constraints of a 3D kinematic model of the human arm. The motion of the arm is then derived as a transition between the arm postures. Applications of these results are foreseen in the analysis of human motion patterns. Received: 26 January 1996 / Accepted: 17 July 1997     

Reconstruction of 3D interacting solids of revolution from 2D orthographic views

3D CAD is replacing 2D CAD to improve efficiency of product design and manufacturing. Therefore, converting legacy 2D drawings into 3D solid models is required. CSG based approaches reconstruct solid models from orthographic views more efficiently than traditional B-rep based approaches. A major limitation of CSG based approaches has been the limited domain of objects that can be handled. This paper aims at extending the capabilities of CSG based approaches by proposing a hint-based recognition of interacting solids of revolution. This approach can handle interacting solids of revolution as well as isolated solids of revolution.     

A 2-point algorithm for 3D reconstruction of horizontal lines from a single omni-directional image

Reconstruction of 3D scenes with abundant straight line features has many applications in computer vision and robot navigation. Most approaches to this problem involve stereo techniques, in which a solution to the correspondence problem between at least two different images is required. In contrast, 3D reconstruction of straight horizontal lines from a single 2D omni-directional image is studied in this paper. The authors show that, for symmetric non-central catadioptric systems, a 3D horizontal line can be estimated using only two points extracted from a single image of the line. One of the two points is the symmetry point of the image curve of horizontal line, and the other is a generic point on the image curve. This paper improves on several prior works, including horizontal line detection in omni-directional image and line reconstruction from four viewing rays, but is simpler than those methods while being more robust. We evaluate how the precision of feature point extraction can affect line reconstruction accuracy, and discuss preliminary experimental results.     

A new approach to 3D reconstruction without camera calibration

In this paper, we present a new approach for 3D scene reconstruction based on projective geometry without camera calibration. Previous works use at least six points to build two projective reference planes. Our contribution is to reduce the number of reference points to four by exploiting some geometrical shapes contained in the scene. The first implemented algorithm allows the reconstruction of a fourth point on each reference plane. The second algorithm is devoted to the 3D reconstruction. We obtained the expected good results and the proposed method is to equip a mobile robot moving in a structured environment.     

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature.When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and ex...     

3D reconstruction of polyhedral objects from single parallel projections using cubic corner

This paper presents a direct method to recover the geometry of the 3D polyhedron depicted in a single parallel projection. It uses two sets of information, the list of faces in the object, obtained automatically from the drawing, and a user-identified cubic corner, to compute for the coordinates of the vertices in the drawing and thus establish the 3D geometry of the whole polyhedron. The algorithm exploits the topological structure of the polyhedron, implicit in the connectivities between the faces, resulting in a complexity that is linear in the number of faces. The method is extended to objects with no cubic corners as well. The algorithm works well for recovering objects from accurate line drawings, producing accurate 3D objects. A simple extension to the algorithm allows it to handle inaccurate drawings such as sketches, and produce 3D objects that are consistent with our human perception of the drawings.     

基于单张照片的三维人脸重建优化算法

传统人脸三维重建算法难以确定人脸形状,并且计算复杂。针对此问题,提出一种以水平集方法获取人脸轮廓并结合明暗恢复形状(SFS)算法重建三维模型的方法,该方法仅需单张正面人脸照片。首先采用主动形状模型确定人脸轮廓,将其作为水平集的初始演化曲线,分割出完整的人脸形状;然后对人脸区域进行灰度变换,求出灰度图像;最后通过SFS算法重建已知光照条件的人脸图像的三维模型,将该模型作为参考与灰度图像匹配,进而确定其光照条件和三维模型。实验结果表明,与基于网格模型的算法相比,该方法可快速地重建具有完整形状的人脸模型。