Abstract
Abstract. Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.
Highlights
Modelling 3D urban structures gained popularity in urban monitoring, safety, planning, entertainment and commercial applications. 3D models are valuable especially for simulations
One challenge in creating realistic models is registering 2D optical imagery with the 3D LIDAR imagery. This can be formulated as a camera pose estimation problem where the transformation between 3D LIDAR coordinates and 2D image coordinates is characterized by camera parameters
We propose a system for this case and we represent a possible case story on a sample data set including an iPhone image and LIDAR point cloud of an urban structure
Summary
Modelling 3D urban structures gained popularity in urban monitoring, safety, planning, entertainment and commercial applications. 3D models are valuable especially for simulations. Some of the significant studies in this field include the alignment work Huttenlocher and Ullman (1990) and the viewpoint consistency constraint Lowe (1987) matched the projections of a known 3D model to 2D edge images. In the area of single-view registration, Vasile et al (2006) introduced LIDAR data to derive a pseudo-intensity image with shadows for correlation with aerial imagery. Their registration procedure starts with GPS and camera line of sight information and uses an exhaustive search over translation, scale, and lens distortion. The tasks numbers next to the flow chart steps will be referred in the rest of the article to reduce the complexity of the framework description
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