  Recently, significant progress has been made in single-view depth estimation
thanks to increasingly large and diverse depth datasets. However, these
datasets are largely limited to specific application domains (e.g. indoor,
autonomous driving) or static in-the-wild scenes due to hardware constraints or
technical limitations of 3D reconstruction. In this paper, we introduce the
first depth dataset DynOcc consisting of dynamic in-the-wild scenes. Our
approach leverages the occlusion cues in these dynamic scenes to infer depth
relationships between points of selected video frames. To achieve accurate
occlusion detection and depth order estimation, we employ a novel occlusion
boundary detection, filtering and thinning scheme followed by a robust
foreground/background classification method. In total our DynOcc dataset
contains 22M depth pairs out of 91K frames from a diverse set of videos. Using
our dataset we achieved state-of-the-art results measured in weighted human
disagreement rate (WHDR). We also show that the inferred depth maps trained
with DynOcc can preserve sharper depth boundaries.

Computer Vision and Pattern Recognition