Identifying Planes in Point-Clouds for Efficient Hybrid Rendering

Abstract

We present a hybrid rendering technique for highfeature colored point clouds that achieves both, high performance and high quality. Planar subsets in the point cloud are identified to drastically reduce the number of vertices, thus saving transformation bandwidth at the cost of the much higher fill-rate. Moreover, when rendering the planes, the filtering is comparable to elaborate point-rendering methods but significantly faster since it is supported in hardware. This way we achieve at least a 5 times higher performance than simple point rendering and a 40 times higher than a splatting technique with comparable quality. The preprocessing performance is orders of magnitude faster than comparable high quality point cloud simplification techniques. The plane detection is based on the random sample consensus (RANSAC) approach, which easily finds multiple structures without using the expensive Hough transform. Additionally, we use an octree in order to identify planar representations at different scales and accuracies for level-of-detail selection during rendering. The octree has the additional advantage of limiting the number of planar structures, thereby making their detection faster and more robust. Furthermore, the spatial subdivision facilitates handling out-of-core point clouds, both in preprocessing and rendering.