  Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on
handcrafted visual features or raw RGB values for establishing correspondences
between images. These features, while suitable for sparse mapping, often lead
to ambiguous matches in texture-less regions when performing dense
reconstruction due to the aperture problem. In this work, we explore the use of
learned features for the matching task in dense monocular reconstruction. We
propose a novel convolutional neural network (CNN) architecture along with a
deeply supervised feature learning scheme for pixel-wise regression of visual
descriptors from an image which are best suited for dense monocular SLAM. In
particular, our learning scheme minimizes a multi-view matching cost-volume
loss with respect to the regressed features at multiple stages within the
network, for explicitly learning contextual features that are suitable for
dense matching between images captured by a moving monocular camera along the
epipolar line. We integrate the learned features from our model for depth
estimation inside a real-time dense monocular SLAM framework, where photometric
error is replaced by our learned descriptor error. Our extensive evaluation on
several challenging indoor datasets demonstrate greatly improved accuracy in
dense reconstructions of the well celebrated dense SLAM systems like DTAM,
without compromising their real-time performance.
