Visibility Learning in Large-Scale Urban Environment
A crucial step in many vision based ap- plications, such as localization and structure from motion, is the data association between a large map of known 3D points and 2D features perceived by a new camera. In this paper, we propose a novel approach to predict the visibility of known 3D points with respect to a query camera in large-scale envi- ronments. In our approach, we model the visibility of each 3D point with respect to a camera pose using a memory-based learning algorithm, in which a distance metric between cameras is learned in an entirely non- parametric way. We show that by fully exploiting the geometric relationships between the 3D map and the camera poses, as well as the related appearance information, the resulting prediction is much more robust and efficient than conventional approaches.We demonstrate the performance of our algorithm on a large urban 3D model in terms of both speed and accuracy.
Pablo F. Alcantarilla Kai Ni Luis M. Bergasa Frank Dellaert
Department of Electronics,University of Alcal(a). Alcal(a) de Henares,Madrid,Spain School of Interactive Com- puting,Georgia Institute of Technology,Atlanta,GA 30332,USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
6205-6212
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）