Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a super-resolution of LiDAR depth data. We'll describe the performance of our prediction method, explain how to train the depth prediction network, and showcase examples of its applications. Codes and video demonstration are also publicly available. This session is for registrants who are already familiar with basic machine learning techniques.