欢迎访问《图学学报》 分享到：
图学学报

图学学报

• 计算机视觉 • 上一篇    下一篇

基于深度图像的三维场景重建系统

  

  1. 浙江师范大学数理与信息工程学院，浙江 金华 321004
  • 出版日期:2018-12-31 发布日期:2019-02-20
  • 基金资助:
    国家自然科学基金项目(61170315)

3D Scene Reconstruction System Based on Depth Image

  1. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua Zhejiang 321004, China
  • Online:2018-12-31 Published:2019-02-20

PDF (PC)

140

摘要： 针对计算机图形学和视觉领域研究热点——三维场景重建，首先分析了 Kinect v2 (Kinect for Windows v2 sensor)获取深度图像的原理，说明深度图像噪声的来源。然后根据获取 深度图像的原理设计一种算法对点云采样范围进行裁剪。其次对点云离群点进行去除，填补点 云孔洞，以提高重建质量。常见的三维场景重建大都采用了 KinectFusion 的一个全局立方体方 案，但只能对小范围内的场景进行重建。对此设计了一种对大场景进行点云匹配的 ICP 算法。 最后对点云进行曲面重建，实现一套低成本、精确的针对大场景的三维重建系统。

关键词: Kinect v2, 三维场景重建, 点云去噪, 离群点去除, ICP

Abstract: The reconstruction of three-dimensional scenes is currently a research hotspot in computer graphics and visual fields. Firstly, this paper analyzes the principle of depth image acquisition by Kinect v2 (Kinect for Windows v2 sensor), and explain the source of depth image noise. Then according to the principle of obtaining the depth image, an algorithm is designed to crop the point cloud sampling range. Secondly, the point cloud outliers are removed to fill the voids of the cloud to improve the reconstruction quality. Common three-dimensional scene reconstruction mostly uses a global three-dimensional body scheme of Kinect Fusion. This scheme can only reconstruct scenes in a small area. An ICP algorithm for point cloud matching of large scenes is designed for this purpose. Finally, surface reconstruction is performed on the point cloud to realize a low-cost and accurate three-dimensional reconstruction system for large scenes.

Key words:  Kinect v2, 3D scene reconstruction, point cloud denoising, outlier removal, ICP