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A K-neighbor-based Ray Tracing of Point Clouds

机译：基于K邻居的点云光线跟踪

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摘要

Previous ray tracing methods usually treat point-cloud models with attributes including coordinates, normals and radius of points. While 3D coordinates of points can be precisely acquired by equipments, normals and radius of points need to be computed before ray tracing algorithm. Such computation always takes a long time, and produces various errors. This paper proposes a novel ray tracing method of point-cloud models, based on K-nearest-neighbors of iterative points. The method locates a finite number of nearest points to the iterative point in a ray, and computes the normal vector of local surface by using area-weighted average of normals of triangles, which consist of the iterative point and the K-nearest points. The intersection and its normal are obtained by firstly computing intersections between ray and regular triangles, and then blending normals of regular sampling points of local surface, which produce smooth rendering effect and represent more geometric details. Numerical experiments show that our method ensures coherence of normals of intersections, especially for half-open or fragmented point clouds. Moreover, our method can progressively render point clouds in multi-resolution mode. We employ a balanced binary tree to locate the nearest points, and employ grids to avoid unnecessarily iterative computation in the space of point clouds.
机译：以前的射线追踪方法通常使用属性包括点的坐标，法线和半径来处理点云模型。虽然设备可以精确获取点的3D坐标，但在光线跟踪算法之前需要计算点的法线和半径。这样的计算总是花费很长时间，并且产生各种错误。基于迭代点的K近邻，提出了一种新的点云模型光线跟踪方法。该方法在射线中找到到迭代点的有限数量的最近点，并使用三角形法线的面积加权平均值计算局部表面的法线向量，该三角形法线由迭代点和K最近点组成。通过首先计算射线与正三角形之间的交点，然后混合局部曲面的正则采样点的法线来获得交点及其法线，从而产生平滑的渲染效果并表示更多的几何细节。数值实验表明，我们的方法可以确保相交法线的连贯性，特别是对于半开或零散的点云。而且，我们的方法可以在多分辨率模式下逐步渲染点云。我们使用平衡的二叉树来定位最近的点，并使用网格来避免在点云空间中不必要的迭代计算。

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  • 来源
    《Journal of information and computational science》 |2015年第13期|4929-4941|共13页
  • 作者

    Peng Cai; Dehui Kong; Shaofan Wang; Baocai Yin; Yi Huo;

  • 作者单位

    Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China;

    Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China;

    Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China;

    Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China;

    Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Point Clouds; K-neighbor-based; Balanced Binary Tree; Local Surface;

    机译：点云;基于K邻居的;平衡二叉树;局部表面;

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