  This thesis introduces the wide multiple baseline stereo (WxBS) problem.
WxBS, a generalization of the standard wide baseline stereo problem, considers
the 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, evaluation metric and baselines has been introduced.
  The thesis presents the following improvements of the WxBS pipeline. (i) A
loss function, called HardNeg, for learning a local image descriptor that
relies on hard negative mining within a mini-batch and on the maximization of
the distance between the closest positive and the closest negative patches.
(ii) The descriptor trained with the HardNeg loss, called HardNet, is compact
and shows state-of-the-art performance in standard matching, patch verification
and retrieval benchmarks. (iii) A method for learning the affine shape,
orientation, and potentially other parameters related to geometric and
appearance properties of local features. (iv) A tentative correspondences
generation strategy which generalizes the standard first to second closest
distance ratio is presented. The selection strategy, which shows performance
superior to the standard method, is applicable to either hard-engineered
descriptors like SIFT, LIOP, and MROGH or deeply learned like HardNet. (v) A
feedback loop is introduced for the two-view matching problem, resulting in
MODS -- matching with on-demand view synthesis -- algorithm. MODS is an
algorithm that handles a viewing angle difference even larger than the previous
state-of-the-art ASIFT algorithm, without a significant increase of
computational cost over "standard" wide and narrow baseline approaches. Last,
but not least, a comprehensive benchmark for local features and robust
estimation algorithms is introduced.
