SoftPOSIT方法在PNP问题中的应用

本文针对PNP问题(N>6且不共面)提出了一种基于SoftPOSIT方法的技术方案,内容包括:1)以一长方体为目标物体,建立目标物体坐标系,确定目标物体坐标系下角点的空间坐标;2)建立包含透镜畸变分析的摄像机针孔成像模型,采集目标物体图像;3)获取物体角点的图像坐标,在目标物体角点的物点和像点对应关系未知的情况下,利用SoftPOSIT方法迭代出摄像机在目标物体坐标系下的全局最优位姿参数;4)根据得到的摄像机在目标物体坐标系下的位姿参数,计算目标物体角点在摄像机坐标系下的坐标。本文的研究结果表明,该方法为解决PNP问题(N>6)提供了一个理论上可行且有效的方法。     

三维视觉传感器多视角曲面测量数据的一种配准拼接方法

本文提出一种将三维视觉传感器多视角曲面测量数据进行拼接从而获得被测物体整个表 面模型的方法．首先在给定不同视角之间初始坐标旋转矩阵R和平移矢量T的基础上,采用一 种迭代配准方法求解更为准确的R和T,从而将不同视角下测量三维数据转换到同一坐标系下 ;然后通过引入三维空间的球坐标系,将配准后物体表面数据按其空间角度进行网格化处理 ,得到覆盖整个物体表面的数据网格．本文最后给出了配准建模实例．     

三维图像虚拟视点生成优化研究仿真

为完整获取立体场景中所有视点信息,提出基于几何模型的立体图像虚拟视点生成方法.结合空间距离与像素差值两个影响因素,建立双边滤波器的滤波函数,定义权重系数,调节邻近度因子与亮度相似性因子的衰减性,得到滤波后的像素值;采集不同视角下二维图像,确定三维空间与二维图像间的几何映射关系,经过计算横向与纵向曲率、物体横向分割等过程...     

基于单幅图像明暗恢复形状算法设计

以单幅二维图像为研究对象,首先将彩色图像转换成灰度图像,然后直接寻找灰度值与缺失的第三维信息之间的关系,最后根据这种关系来求出物体表面缺失的第三维信息。整个算法流程简单,速度非常快,形状恢复满意度高。     

三维曲面的特征描述和特征匹配

本文提出了一种新的三维曲面特征描述算法,将二维图像上的特征描述思想推广到三维网格。算法将三维网格表示成从顶点到高斯曲率的映射函数,从而获得可类比于二维图像的相似性。借助于法线和梯度建立局部球坐标系,通过二维统计直方图对特征点邻域的几何信息进行描述,使得特征描述具有平移、旋转和缩放不变性,最终生成128维的特征向量(特征描述符)。基于特征向量,我们实现了多分辨率和异拓扑网格下的特征匹配,展示并分析了实验结果。本文的研究动机来源于三维扫描建模以及多视点三维重建技术中对特征描述和特征匹配的需求,主要的应用方向包括:扫描配准、模型注册、动画跟踪、对称检测和模型检索。     

对平面物体多图象数据进行拼接的方法

通过对平面物体拍摄多幅图象,利用视觉标定技术,求出物体在世界坐标系下的坐标,消除了由于摄像头参数不同对图象拼接造成的影响,实现了在世界坐标系下构造实际物体整体的目的;同时利用具有视觉不变性的特征量,解决了拼接时数据的取舍问题。     

对平面物体多图象数据进行拼接的方法

通过对平面物体拍摄多幅图象,利用视觉标定技术,求出物体在世界坐标系下的坐标,消除了由于摄像头参数不同对图象拼接造成的影响,实现了在世界坐标系下构造实际物体整体的目的;同时利用具有视觉不变性的特征量,解决了拼接时数据的取舍问题.     

基于 2 个灭点和局部尺度的三维空间尺度估计

三维空间尺度估计是三维重建中的一个重要工作,现实世界中也存在一些基于单幅图像进行三维空间尺度估计的需求。通常情况下,尺度估计需先对相机进行标定。根据单目图像符合透视原理的特性,提出了一种基于 2 个灭点和局部尺度信息的方法对相机进行标定,从而得到单目图像物体中三维空间尺度信息的估计。首先,从单目图像中选择 2 组互相正交的平行线组,得到对应 2 个灭点的坐标;然后,利用灭点坐标和焦距信息得到世界坐标系和相机坐标系之间的旋转矩阵,再利用灭点的性质和已知局部尺度信息得到平移向量,完成单目相机的标定;最后,还原二维图像中像素点对应的三维世界坐标值,计算出图像中 2 个像素点在三维空间的尺度信息。实验结果表明,该方法能有效地对单幅图像中的建筑物体进行尺度估计。     

基于摄影原理的三维纹理重建技术研究

讨论的是基于摄影原理的三维物体表面纹理的重建技术.该技术从国内外研究现状和主要不足出发,对三维物体表面纹理的采集和映射方法进行研究和改进,提出了较为合理的算法.纹理采集过程中分析了回转工作台的结构、纹理采集的原则并建立相关的坐标系;根据拍照和投影变换的原理建立三个坐标系对应点间的关系、推导出坐标变换关系式和转换矩阵;对模型表面微小面片法向量进行均匀化处理;通过计算面片的法向量与纹理采集方向向量的位置关系确定其最佳纹理,最后通过多角度映射达到三维物体表面纹理重建的目的.     

一种基于深度和颜色的3D表面重建方法

为了快速、有效地对三维物体的表面进行重建，提出了一种基于深度和颜色的3D表面重建方法。该方法利用同一物体在空间稀疏分布的不同视点下的多幅深度图像，根据3D物体表面同一点在不同深度图像上颜色和纹理的一致性，通过逆投影变换，可将图像中像素点映射到三维空间中的正确位置。然后在以物体为中心的单个坐标系下，进行数据融合，可得到三维物体表面各点的三维数据。实验结果表明该方法具有复杂度恒定、获取图像真实感强等优点，适用于形状、结构草杂的物体重建。     

Robust 3D face capture using example-based photometric stereo

We show that using example-based photometric stereo, it is possible to achieve realistic reconstructions of the human face. The method can handle non-Lambertian reflectance and attached shadows after a simple calibration step. We use spherical harmonics to model and de-noise the illumination functions from images of a reference object with known shape, and a fast grid technique to invert those functions and recover the surface normal for each point of the target object. The depth coordinate is obtained by weighted multi-scale integration of these normals, using an integration weight mask obtained automatically from the images themselves. We have applied these techniques to improve the PhotoFace system of Hansen et al. (2010).     

Surface mapping brain function on 3D models

A flexible graphics system for displaying functional and anatomic data on arbitrary collections of surfaces on or within the brain is presented. The system makes it possible to show complex, convoluted surfaces with the shading cues necessary to understand their shapes; to vary viewpoint, object position, illumination, and perspective easily; to show multiple-objects in one view, with or without transparency, in order to examine internal surfaces and intersecting objects in relation to each other; and to superimpose quantitative information on biological or otherwise defined surfaces anywhere within the volume, thus furthering understanding of both quantitative and positional information in its global context. These display techniques are applied to a new form of biological surface model, the removed surface. The surface-removal method creates a set of surfaces internal to a given object, so that, given a specified distance, every point on the created surfaces is equidistant from the surface of the enclosing object. The method is based on thresholding a derived scalar field, the minimum distance field. Models made by this method have applications in 3-D neurobiology and provide an alternative to cutaways for viewing patterns of internal functional activity     

FSAM: A fast self-adaptive method for correcting non-uniform illumination for 3D reconstruction

Since three-dimensional surface texture can display texture information of an object better than its two-dimensional counterpart and can vary with scene illumination and the view angles, it is widely used in virtual reality, computer games and animation applications. Photometric Stereo, as one of the most effective technologies for capturing three-dimensional surface information, has attracted wide attention both from academic researchers and industrial fields. Uniform illumination is the essential condition for capturing and reconstructing surface heightmaps. In practice, non-uniform illumination leads to distorted surface heightmaps, such as distortion and aberration during the capture and reconstruction processes. This paper proposes and assesses a fast and self-adaptive method based on inverse-square law to correct non-uniform illumination for reconstruction of 3D surface heightmaps, and to eliminate the distortions. In order to objectively assess the performance of the illumination correction algorithm, average absolute gradient (AAG) is proposed to compare the surface heightmaps reconstructed using corrected illumination with those reconstructed without illumination correction. Experimental results show that the method is efficient and can produce convincing results.     

室内环境下基于R-CNN的光照自适应物体检测

物体检测是工作于室内环境的移动机器人必须解决的问题。物体检测受到环境动态变化的影响,其中尤以光照变化的影响最为明显。分析室内环境中光照变化特点,研究如何通过提取图像空间特征快速识别环境中光照状况,并以光照识别结果控制物体检测模式切换,在不同光照状态下,自适应地选择使用图像传感器或者激光传感器数据,结合深度学习的特征选择能力,保证物体检测性能。机器人运行时,首先通过提取图像在CIEXYZ空间Y分量上的统计特征,并结合一些其他特征,实现快速地对图像拍摄时所处环境的光照状态进行估计;在光照适中的情况下,利用R-CNN算法结合移动机器人特点,实现在图像空间下的快速物体检测;在光照不足或过强时,先把三维激光传感器获取的点云转换成深度图像,再利用R-CNN算法实现物体检测。实验结果表明了所提出算法的有效性。     

模拟绘画的三维几何纹理生成研究

该文提出一种模拟绘画的三维几何纹理生成方法。该方法的主要思想如下：（1）对物体表面进行近似等距离分点；（2）假设在各分点处放置一个个小球，利用这些小球来获得物体表面的总体明暗信息；（3）建立各分点处画元的绘制模式；（4）在绘制阶段，利用所获得的光照信息和画元的绘制模式在各分点处绘制画元，以获得符合物体表面总体明暗调子的几何纹理；（5）在上述工作的基础上，通过对画元实施一系列操作来获得更为复杂的几何纹理。与传统方法相比，该文所给出的几何纹理生成技术具有对信息处理的同一性、可对画面信息进行离散处理、所生成的纹理更具立体感和速度快等特性。实验结果验证了方法的有效性。     

Eigen-Texture method: appearance compression and synthesis based ona 3D model

Image-based and model-based methods are two representative rendering methods for generating virtual images of objects from their real images. However, both methods still have several drawbacks when we attempt to apply them to mixed reality where we integrate virtual images with real background images. To overcome these difficulties, we propose a new method, which we refer to as the Eigen-Texture method. The proposed method samples appearances of a real object under various illumination and viewing conditions, and compresses them in the 2D coordinate system defined on the 3D model surface generated from a sequence of range images. The Eigen-Texture method is an example of a view-dependent texturing approach which combines the advantages of image-based and model-based approaches. No reflectance analysis of the object surface is needed, while an accurate 3D geometric model facilitates integration with other scenes. The paper describes the method and reports on its implementation     

RGBD融合明暗恢复形状的全视角三维重建技术研究

为了高效、高精度、低成本地实现对物体的全视角三维重建, 提出一种使用深度相机融合光照约束实现全视角三维重建的方法。该重建方法中,在进行单帧重建时采用RGBD深度图像融合明暗恢复形状(Shape from shading,SFS)的重建方法, 即在原有的深度数据上加上额外的光照约束来优化深度值; 在相邻两帧配准时, 采用快速点特征直方图（Fast point feature histograms, FPFH）特征进行匹配并通过随机采样一致性（Random sample consensus, RANSAC）滤除错误的匹配点对求解粗配准矩阵, 然后通过迭代最近点（Iterative closest point, ICP）算法进行精配准得出两帧间的配准矩阵; 在进行全视角的三维重建时, 采用光束平差法优化相机位姿, 从而消除累积误差使首尾帧完全重合, 最后融合生成一个完整的模型。该方法融入了物体表面的光照信息,因此生成的三维模型更为光顺,也包含了更多物体表面的细节信息,提高了重建精度;同时该方法仅通过单张照片就能在自然光环境下完成对多反射率三维物体的重建,适用范围更广。本文方法的整个实验过程通过手持深度相机就能完成,不需要借助转台,操作更加方便。     

基于梯度方向角量化的匹配新算法

提出了一种在复杂光照环境下具备旋转不变识别能力的匹配新算法。图像梯度方向角是一种在光照变化及目标发生旋转的情况下保持相对稳定的特征量。通过对该特征量的量化与统计分析，分2步实现在变化的光照条件下旋转目标的识别：(1)统计子图像块梯度方向角量化值的直方图，大概确定出目标旋转的角度；(2)利用确定的角度值对模板图像的梯度方向角量化值与子图像的量化值进行旋转匹配。实验证明该算法具有较强的鲁棒性。     

3D object segmentation using B-Surface

3D object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3D object segmentation. An improved 3D external force field combined with the normalized GVF is utilized. After the initialization of a surface model near the target, B-Surface starts to deform to locate the boundary of the object. First, it overcomes the difficulty that comes from analyzing 3D volume image slice by slice. And the speed of B-Surface deformation is enhanced since the internal forces are not needed to compute in every iteration deformation step. Next, the normal at every surface point can be calculated easily since B-Surface is a continuous deformable model. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Experimental results and analysis are presented in this paper. We can see that the B-Surface algorithm can find the surface of the target efficiently.     

Estimating Specular Roughness and Anisotropy from Second Order Spherical Gradient Illumination

This paper presents a novel method for estimating specular roughness and tangent vectors, per surface point, from polarized second order spherical gradient illumination patterns. We demonstrate that for isotropic BRDFs, only three second order spherical gradients are sufficient to robustly estimate spatially varying specular roughness. For anisotropic BRDFs, an additional two measurements yield specular roughness and tangent vectors per surface point. We verify our approach with different illumination configurations which project both discrete and continuous fields of gradient illumination. Our technique provides a direct estimate of the per-pixel specular roughness and thus does not require off-line numerical optimization that is typical for the measure-and-fit approach to classical BRDF modeling.