Advanced plane properties by using level image

This paper deals with advanced plane properties in analyzed point clouds. Planes are detected by the level connected component labeling, which is our modification of the classical algorithm for 3D data. According selected detection parameters and a scanning dimension the algorithm detects individual levels in an input point cloud. A level is presented by an image expressing the points' presence at a specific level in a space, we call it level image. The pixel size in a level image is equal to the point cloud distance quantization. This connection allows us to use image processing methods to get important properties about the analyzed 3D space, which can be difficult to get by the standard mathematical solution. Image processing methods offers a simple plane segmentation and visualization or area and perimeter estimation including statistical data description. The results show level image advantages.