The majority of prior monocular depth estimation methods without groundtruth
depth guidance 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 an off-the-shelf relative
depth estimator that produces structured but metric-agnostic depth. By
combining structure distillation with a 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, as well as in downstream
applications using our depth maps. This work provides a full study, covering
methods, data, and applications aspects.

本文提出一种基于结构蒸馏和左右一致性相结合的单目深度估计方法，实现了针对室内场景的结构化和度量深度，并通过 SimSIN 数据集进行了验证。