Boosting Monocular Depth with Panoptic Segmentation Maps

Abstract

Monocular depth prediction is ill-posed by nature; hence successful approaches need to exploit the available cues to the fullest. Yet, real-world training data with depth ground-truth suffers from limited variability and data acquired from depth sensors is also sparse and prone to noise. While available datasets with semantic annotations might help to better exploit semantic cues, they are not immediately usable for depth prediction. We show how to leverage panoptic segmentation maps to boost monocular depth predictors in stereo training setups. In particular, we augment a self-supervised training scheme through panoptic-guided smoothing, panoptic-guided alignment, and panoptic left-right consistency from ground truth or inferred panoptic segmentation maps. Our approach incurs only a minor overhead, can easily be applied to a wide range of depth estimation methods that are trained at least partially using stereo pairs, providing a substantial boost in accuracy.