Building LiDAR point cloud denoising processing through sparse representation

Nowdays, airborne LiDAR comes into a popular way to survey the ground scene, particularly for the application of building reconstruction. However, the LiDAR point cloud acquired is usually polluted by noise for the existence of LiDAR system's inherent error and aircraft's shock. Thus, before LiDAR data is used, a preprocessing such as denoising is needed. This paper focus on the denoising of building LiDAR data. First, the building LiDAR point cloud is rasterized into a two- dimensional image. Then, a dictionary learned from training samples is used to denoise the image according to signal's sparse representation theory. Last, we can get the building's raster image with little noise.