三维虚拟现实物体表面图像点云数据提取仿真

Simulation of Point Cloud Data Extraction From 3D Virtual Reality Object Surface Image

三维虚拟物体图像表面提取过程中,受环境光照、噪声等因素的干扰,导致物体提取准确度降低。为解决上述问题,提出一种三维虚拟现实物体表面图像点云数据提取方法。结合半径滤波和DBSAN聚类方法,删除图像点云数据内的噪声。采用八叉树方法划分点云数据空间,将点云包围在不同立方体包围盒中。通过最小二乘二次曲面拟合局部近邻点,获取候选点在拟合曲线的估计法相矢量,同时根据各个点法相矢量和近邻点法相矢量之间的夹角变化,提取三维虚拟现实物体表面图像点云数据。实验结果表明,所提方法可以准确提取点云数据,且提取时间低于0.25s。

In the process of extracting 3D virtual object images,the accuracy of object extraction may be reduced due to environmental lighting and noise.To solve the problem,this paper proposed a method for extracting point cloud data from the surface image of a three-dimensional virtual reality object.Combining the radius filtering with the DBSAN clustering method,we deleted the noise within the image point cloud data.Then,we used an octree method to partition the space of point cloud data,so that the point cloud could be enclosed in different cube bounding boxes.After ftting the local neighboring points with a least squares quadratic surface,we got the estimated normal vector of candidate points on the fitting curve.According to the change of angle between the normal vector of each point and its neighboring normal vector,we extracted the point cloud data of the 3D virtual reality object surface image.Experimental results show that the proposed method can accurately extract point cloud data,and the extraction time is less than 0.25s.