Fusion of Auxiliary Imaging Information for Robust, Scalable and Fast 3D Reconstruction

One of the potentially effective means for 3D reconstruction is to reconstruct the scene in a global manner, rather than incrementally, by fully exploiting available auxiliary information on imaging condition, such as camera location by GPS, orientation by IMU(or Compass), focal length from EXIF etc. However these auxiliary information, though informative and valuable, is usually too noisy to be directly usable. In this paper, we present a global method by taking advantage of such noisy auxiliary information to improve SfM solving. More specifically, we introduce two effective iterative optimization algorithms directly initiated with such noisy auxiliary information. One is a robust iterative rotation estimation algorithm to deal with contaminated EG(epipolar graph), the other is a robust iterative scene reconstruction algorithm to deal with noisy GPS data for camera centers initialization. We found that by exclusively focusing on the inliers estimated at the current iteration, called potential inliers in this work, the optimization process initialized by such noisy auxiliary information could converge well and efficiently. Our proposed method is evaluated on real images captured by UAV(unmanned aerial vehicle), StreetView car and conventional digital cameras. Extensive experimental results show that our method performs similarly or better than many of the state-of-art reconstruction approaches, in terms of reconstruction accuracy and scene completeness, but more efficient and scalable for large-scale image datasets.