Voxel planes: Rapid visualization and meshification of point cloud ensembles
Voxel planes: Rapid visualization and meshification of point cloud ensembles
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体素平面：点云集合的快速可视化和网格化
DOI：
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发表时间：
2013
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通讯作者：
Robert W. Platt
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作者：
J. Ryde;Vikas Dhiman;Robert W. Platt
在移动机器人感知处理管道中，特别是随着RGB-D（颜色和深度）图像传感器的快速采用，越来越需要将无组织的点云转换为表面重建。为了以批处理的方式处理桌面扫描仪产生的点云，许多当代的方法源于计算机图形学社区的工作。移动机器人的需求是不同的，包括支持实时处理、增量更新、定位、映射、路径规划、避障、光线追踪、地形可穿越性评估、抓取/操作和可视化，以实现有效的人机交互。我们对贪心投影和行进立方体以及我们的体素平面方法进行了定量比较。评估了这些算法的执行速度、误差、压缩和可视化外观。我们的体素平面方法首先计算体素内点上的PCA，并在滑动窗口中结合2×2×2体素邻域的这些PCA结果。其次，最小特征向量和体素质心定义一个与体素相交的平面，重建该体素内的表面斑块（3-6面凸多边形）。根据它们的构造性质，这些表面斑块镶嵌在一起，以产生下面点的表面表示。在公共数据集的实验中，体素平面方法比行进立方体快3倍，比贪婪投影提供300倍的压缩，比行进立方体低10倍的误差，同时允许增量地图更新。
Conversion of unorganized point clouds to surface reconstructions is increasingly required in the mobile robotics perception processing pipeline, particularly with the rapid adoption of RGB-D (color and depth) image sensors. Many contemporary methods stem from the work in the computer graphics community in order to handle the point clouds generated by tabletop scanners in a batch-like manner. The requirements for mobile robotics are different and include support for real-time processing, incremental update, localization, mapping, path planning, obstacle avoidance, ray-tracing, terrain traversability assessment, grasping/manipulation and visualization for effective human-robot interaction. We carry out a quantitative comparison of Greedy Projection and Marching cubes along with our voxel planes method. The execution speed, error, compression and visualization appearance of these are assessed. Our voxel planes approach first computes the PCA over the points inside a voxel, combining these PCA results across 2×2×2 voxel neighborhoods in a sliding window. Second, the smallest eigenvector and voxel centroid define a plane which is intersected with the voxel to reconstruct the surface patch (3-6 sided convex polygon) within that voxel. By nature of their construction these surface patches tessellate to produce a surface representation of the underlying points. In experiments on public datasets the voxel planes method is 3 times faster than marching cubes, offers 300 times better compression than Greedy Projection, 10 fold lower error than marching cubes whilst allowing incremental map updates.