Automatic registration of aerial imagery with untextured 3D LiDAR models

A fast 3D model reconstruction methodology is desirable in many applications such as urban planning, training, and simulations. In this paper, we develop an automated algorithm for texture mapping oblique aerial images onto a 3D model generated from airborne Light Detection and Ranging (LiDAR) data. Our proposed system consists of two steps. In the first step, we combine vanishing points and global positioning system aided inertial system readings to roughly estimate the extrinsic parameters of a calibrated camera. In the second step, we refine the coarse estimate of the first step by applying a series of processing steps. Specifically, We extract 2D corners corresponding to orthogonal 3D structural corners as features from both images and the untextured 3D LiDAR model. The correspondence between an image and the 3D model is then performed using Hough transform and generalized M-estimator sample consensus. The resulting 2D corner matches are used in Lowe's algorithm to refine cam...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Min Ding, Kristian Lyngbaek, Avideh Zakhor
• Keywords: Computer Vision, CVPR 2008, Downtown Berkeley Area, Fast 3d Model, Oblique Aerial Images, Overall 61% Accuracy, Untextured 3d Lidar