  We propose a new dataset for learning local image descriptors which can be
used for significantly improved patch matching. Our proposed dataset consists
of an order of magnitude more number of scenes, images, and positive and
negative correspondences compared to the currently available Multi-View Stereo
(MVS) dataset from Brown et al. The new dataset also has better coverage of the
overall viewpoint, scale, and lighting changes in comparison to the MVS
dataset. Our dataset also provides supplementary information like RGB patches
with scale and rotations values, and intrinsic and extrinsic camera parameters
which as shown later can be used to customize training data as per application.
We train an existing state-of-the-art model on our dataset and evaluate on
publicly available benchmarks such as HPatches dataset and Strecha et
al.\cite{strecha} to quantify the image descriptor performance. Experimental
evaluations show that the descriptors trained using our proposed dataset
outperform the current state-of-the-art descriptors trained on MVS by 8%, 4%
and 10% on matching, verification and retrieval tasks respectively on the
HPatches dataset. Similarly on the Strecha dataset, we see an improvement of
3-5% for the matching task in non-planar scenes.
