Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using vanishing points for camera calibration
International Journal of Computer Vision
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
An Improved Power Cepstrum Based Stereo Correspondence Method for Textured Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Computer and Robot Vision
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To construct complete three-dimensional representations from multiple range images or to employ color images as texture maps for surface models, the relative positions of the sensors used to capture the data must be known, Most data-driven methods for pose estimation require either an accurate initial estimation of the relative orientation be specified or a corresponding set of features be extracted form images, The autonomous identification of corresponding feature positions thus represents the major difficulty in creating completely automated registration and reconstruction systems that place no restrictions on relative sensor positions, In this paper, an automated feature correspondence technique, specifically designed for the task of multi-modal view registration, is presented which requires no initial pose estimates or geometric matching constraints, Both photo-realistic and 3-D scene models are presented that were constructed autonomously by systems employing the described matching algorithm.