Photorealistic Building Reconstruction from Mobile Laser Scanning Data.

Nowadays, advanced real-time visualization for location-based applications, such as vehicle navigation or mobile phone navigation, requires large scale 3D reconstruction of street scenes. This paper presents methods for generating photorealistic 3D city models from raw mobile laser scanning data, which only contain georeferenced XYZ coordinates of points, to enable the use of photorealistic models in a mobile phone for personal navigation. The main focus is on the automated processing algorithms for noise point filtering, ground and building point classification, detection of planar surfaces, and on the key points (e.g., corners) of building derivation. The test site is located in the Tapiola area, Espoo, Finland. It is an area of commercial buildings, including shopping centers, banks, government agencies, bookstores, and high-rise residential buildings, with the tallest building being 45 m in height. Buildings were extracted by comparing the overlaps of X and Y coordinates of the point clouds between the cutoff-boxes at different and transforming the top-view of the point clouds of each overlap into a binary image and applying standard image processing technology to remove the non-building points, and finally transforming this image back into point clouds. The purpose for using points from cutoff-boxes instead of all points for building detection is to reduce the influence of tree points close to the building facades on building extraction. This method can also be extended to transform point clouds in different views into binary images for various other object extractions. In order to ensure the building geometry completeness, manual check and correction are needed after the key points of building derivation by automated algorithms. As our goal is to obtain photorealistic 3D models for walk-through views, terrestrial images were captured and used for texturing building facades. Currently, fully automatic generation of high quality 3D models is still challenging due to occlusions in both the laser and image data and due to significant illumination changes between the images. Especially when the scene contains both trees and vehicles, fully automated methods cannot achieve satisfactory visual appearance. In our approach, we employed the existing software for texture preparation and mapping. [ABSTRACT FROM AUTHOR]