  Accurately describing and detecting 2D and 3D keypoints is crucial to
establishing correspondences across images and point clouds. Despite a plethora
of learning-based 2D or 3D local feature descriptors and detectors having been
proposed, the derivation of a shared descriptor and joint keypoint detector
that directly matches pixels and points remains under-explored by the
community. This work takes the initiative to establish fine-grained
correspondences between 2D images and 3D point clouds. In order to directly
match pixels and points, a dual fully convolutional framework is presented that
maps 2D and 3D inputs into a shared latent representation space to
simultaneously describe and detect keypoints. Furthermore, an ultra-wide
reception mechanism in combination with a novel loss function are designed to
mitigate the intrinsic information variations between pixel and point local
regions. Extensive experimental results demonstrate that our framework shows
competitive performance in fine-grained matching between images and point
clouds and achieves state-of-the-art results for the task of indoor visual
localization. Our source code will be available at [no-name-for-blind-review].
