  We present a novel approach for estimating depth from a monocular camera as
it moves through complex and crowded indoor environments, e.g., a department
store or a metro station. Our approach predicts absolute scale depth maps over
the entire scene consisting of a static background and multiple moving people,
by training on dynamic scenes. Since it is difficult to collect dense depth
maps from crowded indoor environments, we design our training framework without
requiring depths produced from depth sensing devices. Our network leverages RGB
images and sparse depth maps generated from traditional 3D reconstruction
methods to estimate dense depth maps. We use two constraints to handle depth
for non-rigidly moving people without tracking their motion explicitly. We
demonstrate that our approach offers consistent improvements over recent depth
estimation methods on the NAVERLABS dataset, which includes complex and crowded
scenes.
