  Our long term goal is to use image-based depth completion to quickly create
3D models from sparse point clouds, e.g. from SfM or SLAM. Much progress has
been made in depth completion. However, most current works assume well
distributed samples of known depth, e.g. Lidar or random uniform sampling, and
perform poorly on uneven samples, such as from keypoints, due to the large
unsampled regions. To address this problem, we extend CSPN with multiscale
prediction and a dilated kernel, leading to much better completion of
keypoint-sampled depth. We also show that a model trained on NYUv2 creates
surprisingly good point clouds on ETH3D by completing sparse SfM points.
