# Estimating Indoor Scene Depth Maps from Ultrasonic Echoes

arXiv: https://arxiv.org/abs/2409.03336 · HTML: https://mlsanity.com/paper/2409.03336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://mlsanity.com/paper/1709.07492/) (2017)
- [RigNet: Repetitive Image Guided Network for Depth Completion](https://mlsanity.com/paper/2107.13802/) (2021)
- [VisualEchoes: Spatial Image Representation Learning through Echolocation](https://mlsanity.com/paper/2005.01616/) (2020)
- [BatVision: Learning to See 3D Spatial Layout with Two Ears](https://mlsanity.com/paper/1912.07011/) (2019)
- [Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds](https://mlsanity.com/paper/2003.04210/) (2020)
- [Beyond Image to Depth: Improving Depth Prediction using Echoes](https://mlsanity.com/paper/2103.08468/) (2021)
- [3D imaging from multipath temporal echoes](https://mlsanity.com/paper/2011.09284/) (2020)
- [Beyond Visual Field of View: Perceiving 3D Environment with Echoes and Vision](https://mlsanity.com/paper/2207.01136/) (2022)

## Abstract (arXiv)

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.