Non-Rigid Space Resection by Parameterized Models
Traditional photogrammetric space resection requires a minimum of three non-collinear ground control points (GCPs) to determine exterior orientation of a frame image, assuming that the images interior orientation is known. The standard solution is based on collinearity equations which must be linearized and inital approximations must be furnished for the parameters. In this paper, a model based approach to space resection is presented. The method recovers a cameras location and orientation relative to an object coordinate system up to a scale factor without the use of any GCP or vanishing point. The mathematical basis for this approach is the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotatedinterpretation plane in the object space. Described here is a two-step iterative scheme for recovering camera orientation that, unlike existing methods, does not require a good initial guess for the rotation. Instead, the good initial estimate for the rotation is computed directly by using coplanarity constraints. A non-linear least squares minimization procedure is then applied to determine camera orientation accurately. The camera translation and predefined model parameters are determined based on the calculated rotation through a linear least squares minimization. Unlike existing methods, this method does not require a model-to-image fitting process, and is more effective and faster than previous approaches.
Non-rigid Space Resection Parameterized Model Photogrammetry Computer Vision
Ruisheng Wang Vincent Tao Frank P.Ferrie
Centre for Intelligent Machines, McGill University, 3480 University Street, Montreal, Quebec,Canada, Microsoft Corporation, One Microsoft Way Redmond, WA 98052-6399
国际会议
北京国际地理信息系统学术讨论会第七届会议（7th International Workshop Geographical Information System
北京
英文
277-285
2007-09-14（万方平台首次上网日期，不代表论文的发表时间）