Interactive Media Systems, TU Vienna

FT-RANSAC: Towards robust multi-modal homography estimation

By Adam Barclay and Hannes Kaufmann


As the golden standard in robust estimation, the classic RANSAC approach has undergone extensive research that contributed to further enhancements in run-time performance, robustness, and multi-structure support to name a few. Yet, the accelerating growth of multi-modal co-registered datasets requires a new adaptation of the RANSAC algorithm. In this paper, we propose a multi-modal fault-tolerant extension to RANSAC, termed FT-RANSAC, with a model-independent tolerance to degenerate configurations. Besides building on stateof- the-art RANSAC variants, such as PROSAC, our approach introduces a Hough inspired dimensionality reduction and consistency voting processes, to enable robust estimation in the presence of non-homogenous multi-modal correspondence sets. Through experimental evaluation using homography estimation of RGB-D data, we demonstrate that our approach outperforms the classic single-modality RANSAC in robustness and tolerance to degenerate configurations. Finally, the proposed approach lends itself to parallel multi-core implementations, and could be adapted to specialized RANSAC extensions found in the literature.


A. Barclay, H. Kaufmann: "FT-RANSAC: Towards robust multi-modal homography estimation"; Talk: 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Stockholm; 08-24-2014; in: "8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), 2014", IEEE, (2014), 1 - 4.


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