  We introduce a novel RGB-D patch descriptor designed for detecting coplanar
surfaces in SLAM reconstruction. The core of our method is a deep convolutional
neural net that takes in RGB, depth, and normal information of a planar patch
in an image and outputs a descriptor that can be used to find coplanar patches
from other images.We train the network on 10 million triplets of coplanar and
non-coplanar patches, and evaluate on a new coplanarity benchmark created from
commodity RGB-D scans. Experiments show that our learned descriptor outperforms
alternatives extended for this new task by a significant margin. In addition,
we demonstrate the benefits of coplanarity matching in a robust RGBD
reconstruction formulation.We find that coplanarity constraints detected with
our method are sufficient to get reconstruction results comparable to
state-of-the-art frameworks on most scenes, but outperform other methods on
standard benchmarks when combined with a simple keypoint method.
