MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration
VR '01 Proceedings of the Virtual Reality 2001 Conference (VR'01)
Preemptive RANSAC for Live Structure and Motion Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion with Known Camera Positions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Pose Priors for Simultaneously Solving Alignment and Correspondence
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Hill climbing algorithm for random sample consensus methods
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
PALM: portable sensor-augmented vision system for large-scene modeling
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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This paper revisits the pose estimation from point correspondences problem to properly exploit data provided by a GPS. In practice, the location given by the GPS is only a noisy estimate, and some point correspondences may be erroneous. Our method therefore starts from the GPS location estimate to progressively refine the full pose estimate by hypothesizing correct correspondences. We show how the GPS location estimate and the choice of a first random correspondence dramatically reduce the possibility for a second correspondence, which in turn constrains even more the remaining possible correspondences. This results in an efficient sampling of the solution space. Experimental results on a large 3D scene show that our method outperforms standard approaches and a recent related method [1] in terms of accuracy and robustness.