基于Kinect的三维点云数据处理

Written by Congmei Chen Supervised by Jiajun Wang
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第一章
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绪论 .................................................................................................................. 1
With the maturing and popularity of the hardware and software for three dimensional (3D) laser scanning, the corresponding processing technology for 3D point cloud data has become a hot research area. This technology is widely used in computer animation, medical image processing, heritage conservation, terrain exploration, digital media creation and other fields. With the continuous improvement in sampling accuracy and scanning speed, the massive growth in the amount of the acquired point data leads to new challenges and pressures for data processing hardware for the subsequent multi-view registration, surface reconstruction and data storage. In this thesis, a simple and efficient 3D point cloud processing method which includes point cloud registration and surface reconstruction algorithm and can be implemented on mid-and-low-end computers was proposed. Combined with 3D depth somatosensory camera Kinect, it can effectively tackle the problems such as high computing intensity, high complexity, the requirements of high-end computing devices and limited applications in different areas for the traditional processing methods based on the 3D scanning devices. The main research work and contributions are summarized as follows: Firstly, issues regarding the information such as the colors and the depth of the object acquired from the Kinect were discussed along with the specification for software development based on the Kinect in personal computers (PC). Pre-processing algorithms were also proposed for both threshold segmentation and topological relationship establishment. These algorithms are helpful for speeding up the procedures of point cloud registration and surface reconstruction. Secondly, a data registration algorithm based on geometric feature invariants was proposed in this thesis. In this algorithm, the metrics such as the curvature and the neighborhood mark similarity were used to determine the matched point pairs. With the rotation and translation matrices for the initial rigid transformation deduced from the matched point pairs, the point cloud data in different perspectives are registered into the same coordinate system. Then, with the nearest point Iteration Close Point (ICP) algorithm,
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Studies on the Processing of 3D Point Cloud Data based on Kinect
Abstract
rigid transformation for fine registration can be iteratively obtained from the singular value decomposition procedure, with which multi-view registration can be finally implemented. Thirdly, an improved version of the traditional region-growing based surface reconstruction algorithm was proposed. In this algorithm, the initial triangle was constructed with its initial point determined according to the flatness, which enhanced the robustness of the triangulation. This algorithm merged the disconnected regions in searching areas determined upon different definitions of neighborhood systems. Bicubic spline interpolation functions were used to fit surface and repair the holes in the point cloud to improve the quality of grid reconstruction. Finally, the point cloud registration and surface reconstruction algorithms based on the Kinect were implemented with perfect results by hybrid programming in the Visual Studio 2008 development environment and the underlying graphics libraries such as OpenCV and OpenGL. Keywords: Point Clouds Registration, Surface Reconstruction, Geometric Features Invariant, Bicubic Spline Function, Holes Patch
指导老师：王加俊
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Abstract
Studies on the Processing of 3D Point Cloud Data based on Kinect
Studies on the Processing of 3D Point Cloud Data Based on Kinect Abstract
硕士学位论文
论文题目
基于 Kinect 的三维点云数据处理
研究生姓名 指导教师姓名 专 业 名 称 研 究 方 向 论文提交日期
陈聪梅 王加俊 通信与信息系统 数字图像处理 2013 年 4 月
基于 Kinect 的三维点云数据处理
中文摘要
ቤተ መጻሕፍቲ ባይዱ
基于 Kinect 的三维点云数据处理 中文摘要
由于三维激光扫描的软硬件水平日趋成熟和大众化， 相应的三维点云数据的处理 技术成为越来越受关注的研究热点，广泛应用于计算机动画、医学图像处理、文物保 护、 地形勘探和数字化媒体创作等领域。随着扫描设备的采样精度和扫描速度不断提 高，获取的点云数据呈海量增长，导致后期的多视拼接、表面重建和数据存储对硬件 处理设备带来新的挑战和压力。针对目前基于三维扫描设备的数据处理的计算量大、 复杂度高、需要在高性能的设备上才能处理、不能在各个领域广泛应用等问题，并结 合深度体感设备 Kinect，本文提出了一种能够在中低端计算机上实现的简单、高效的 三维点云数据处理方法， 其中包括点云拼接及表面重建算法等关键技术，主要研究工 作和贡献概括如下： 首先，本文从深度体感设备 Kinect 出发，详细介绍了该设备基于 PC 上的开发及 其颜色和深度信息的获取原理， 并对采集得到的点云数据进行阈值分割和拓扑关系的 建立等预处理，大大提高点云拼接及表面重建处理的速度。 其次， 本文提出了一种基于几何特征不变量的拼接方法。该方法通过曲率和邻域 标识相似度等度量来确定匹配点关系， 根据匹配点之间的坐标变换估算初始刚体变换 的旋转和平移矩阵， 将不同视角的点云数据初始配准在同一坐标系下。然后根据最近 点迭代法算法，利用奇异值分解，迭代计算精确配准的刚体变换，以完成多视拼接。 再次， 本文对以往基于区域生长的表面重建算法进行改进：根据平坦度大小确定 初始点构造初始三角形，以增强三角剖分的鲁棒性；通过对邻域的不同定义，选择搜 索范围， 实现非连通区域的连接； 利用双三次样条插值函数拟合曲面， 修复点云孔洞， 改善网格重建质量。 最后，本文在 Visual Studio 2008 开发环境下结合 OpenCV 和 OpenGL 底层图形 库混合编程设计并实现点云拼接及表面重建算法，取得了良好的结果。 关键词：点云拼接、表面重建、几何特征不变量、双三次样条函数、孔洞修复 作 者：陈聪梅
1.1 研究背景及意义 ................................................................................................. 1 1.2 三维点云数据处理技术及现状 .......................................................................... 2 1.3 本文主要研究内容 ............................................................................................. 5 第二章 基于 Kinect 的采数据集及其预处理 ................................................................ 8