We present a method that can evaluate a RANSAC hypothesis in constant time, i.e. independent of the size of the data. A key observation here is that correct hypotheses are tightly clustered together in the latent parameter domain. In a manner similar to the generalized Hough transform we seek to find this cluster, only that we need as few as two votes for a successful detection. Rapidly locating such pairs of similar hypotheses is made possible by adapting the recent 'Random Grids' range-search technique. We only perform the usual (costly) hypothesis verification stage upon the discovery of a close pair of hypotheses. We show that this event rarely happens for incorrect hypotheses, enabling a significant speedup of the RANSAC pipeline. The suggested approach is applied and tested on three robust estimation problems: Camera localization, 3D rigid alignment and 2D-homography estimation. We perform rigorous testing on both synthetic and real datasets, demonstrating an improvement in efficiency without a compromise in accuracy. Furthermore, we achieve state-of-the-art 3D alignment results on the challenging 'Redwood' loopclosure challenge.
|Title of host publication||Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|State||Published - 14 Dec 2018|
|Event||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States|
Duration: 18 Jun 2018 → 22 Jun 2018
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018|
|City||Salt Lake City|
|Period||18/06/18 → 22/06/18|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition