Toward Practical Monocular Indoor Depth Estimation

TL;DR

This paper introduces a robust monocular indoor depth estimation method that combines structure distillation from a relative depth estimator with metric learning, supported by new datasets, achieving real-time performance and improved generalization.

Contribution

It proposes a novel structure distillation approach for indoor depth estimation, integrating metric learning and providing new datasets for training and evaluation.

Findings

Improved depth estimation accuracy in indoor scenes.

Effective generalization to unseen environments.

Real-time inference capability.

Abstract

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.