Automatic registration of aerial imagery with untextured 3D LiDAR models

Min Ding, Kristian Lyngbaek, Avideh Zakhor
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
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
more » ... 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 camera parameters obtained earlier. Our system achieves 91% correct pose recovery rate for 90 images over the downtown Berkeley area, and overall 61% accuracy rate for 358 images over the residential, downtown and campus portions of the city of Berkeley.
doi:10.1109/cvpr.2008.4587661 dblp:conf/cvpr/DingLZ08 fatcat:22dj2pvmyzbzfmu5qbmwjue3ey