WxBS: Wide Baseline Stereo Generalizations
This addresses the challenge of robust image matching across diverse conditions for computer vision applications, representing an incremental advancement with specific improvements.
The paper tackles the problem of wide multiple baseline stereo (WxBS), which involves matching images that differ in multiple factors like viewpoint or illumination, by introducing a new dataset and showing that a combination of RootSIFT and HalfRootSIFT descriptors with MSER and Hessian-Affine detectors works best, with the WxBS-M matcher dominating state-of-the-art methods on new and existing datasets.
We have presented a new problem -- the wide multiple baseline stereo (WxBS) -- which considers matching of images that simultaneously differ in more than one image acquisition factor such as viewpoint, illumination, sensor type or where object appearance changes significantly, e.g. over time. A new dataset with the ground truth for evaluation of matching algorithms has been introduced and will be made public. We have extensively tested a large set of popular and recent detectors and descriptors and show than the combination of RootSIFT and HalfRootSIFT as descriptors with MSER and Hessian-Affine detectors works best for many different nuisance factors. We show that simple adaptive thresholding improves Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them on infrared and low contrast images. A novel matching algorithm for addressing the WxBS problem has been introduced. We have shown experimentally that the WxBS-M matcher dominantes the state-of-the-art methods both on both the new and existing datasets.