Estimating Indoor Scene Depth Maps from Ultrasonic Echoes

TL;DR

This paper introduces a deep learning approach for indoor scene depth estimation using ultrasonic echoes, leveraging audible echoes during training to enhance accuracy despite ultrasonic limitations.

Contribution

The study proposes a novel method that uses audible echoes as auxiliary data during training to improve ultrasonic echo-based depth estimation accuracy.

Findings

Ultrasonic echo-based depth estimation accuracy decreases with higher frequency restrictions.

Using audible echoes during training improves ultrasonic depth estimation accuracy.

Experimental results show significant improvement over baseline methods.

Abstract

Measuring 3D geometric structures of indoor scenes requires dedicated depth sensors, which are not always available. Echo-based depth estimation has recently been studied as a promising alternative solution. All previous studies have assumed the use of echoes in the audible range. However, one major problem is that audible echoes cannot be used in quiet spaces or other situations where producing audible sounds is prohibited. In this paper, we consider echo-based depth estimation using inaudible ultrasonic echoes. While ultrasonic waves provide high measurement accuracy in theory, the actual depth estimation accuracy when ultrasonic echoes are used has remained unclear, due to its disadvantage of being sensitive to noise and susceptible to attenuation. We first investigate the depth estimation accuracy when the frequency of the sound source is restricted to the high-frequency band, and found that the accuracy decreased when the frequency was limited to ultrasonic ranges. Based on this observation, we propose a novel deep learning method to improve the accuracy of ultrasonic echo-based depth estimation by using audible echoes as auxiliary data only during training. Experimental results with a public dataset demonstrate that our method improves the estimation accuracy.