Segmentation based features for wide-baseline multi-view reconstruction

Mustafa, A., Kim, H., Imre, E. and Hilton, Adrian (2015) Segmentation based features for wide-baseline multi-view reconstruction. International Conference on 3D Vision, , Lyon, France. 19 - 22 Oct 2015. pp. 282-290 . (doi:10.1109/3DV.2015.39).

Abstract

A common problem in wide-baseline stereo is the sparse and non-uniform distribution of correspondences when using conventional detectors such as SIFT, SURF, FAST and MSER. In this paper we introduce a novel segmentation based feature detector SFD that produces an increased number of 'good' features for accurate wide-baseline reconstruction. Each image is segmented into regions by over-segmentation and feature points are detected at the intersection of the boundaries for three or more regions. Segmentation-based feature detection locates features at local maxima giving a relatively large number of feature points which are consistently detected across wide-baseline views and accurately localised. A comprehensive comparative performance evaluation with previous feature detection approaches demonstrates that: SFD produces a large number of features with increased scene coverage, detected features are consistent across wide-baseline views for images of a variety of indoor and outdoor scenes, and the number of wide-baseline matches is increased by an order of magnitude compared to alternative detector-descriptor combinations. Sparse scene reconstruction from multiple wide-baseline stereo views using the SFD feature detector demonstrates at least a factor six increase in the number of reconstructed points with reduced error distribution compared to SIFT when evaluated against ground-truth and similar computational cost to SURF/FAST. © 2015 IEEE.

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Contributors

Author: H. Kim

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