Toward Practical Self-Supervised Monocular Indoor Depth Estimation

The majority of self-supervised monocular depth estimation methods focus on driving scenarios. We show that such methods generalize poorly to unseen complex indoor scenes, where objects are cluttered and arbitrarily arranged in the near field. To obtain more robustness, we propose a structure distillation approach to learn knacks from a pretrained depth estimator that produces structured but metric-agnostic depth due to its in-the-wild mixed-dataset training. By combining distillation with the self-supervised branch that learns metrics from left-right consistency, we attain structured and metric depth for generic indoor scenes and make inferences in real-time. To facilitate learning and evaluation, we collect SimSIN, a dataset from simulation with thousands of environments, and UniSIN, a dataset that contains about 500 real scan sequences of generic indoor environments. We experiment in both sim-to-real and real-to-real settings, and show improvements both qualitatively and quantitatively, as well as in downstream applications using our depth maps. This work provides a full study, covering methods, data, and applications. We believe the work lays a solid basis for practical indoor depth estimation via self-supervision.