用超声波测室内深度，结合人耳可听声训练提升精度。
Estimating Indoor Scene Depth Maps from Ultrasonic Echoes
- 用超声波回波测深度，训练时加入可听声辅助数据。
- 实验显示该方法显著提升了超声波测深的准确性。
- 适合需要安静环境或不能发声场景的3D建模应用。
测量室内三维结构通常依赖专用深度传感器，但并非总可用。基于回波的深度估计被视为有前景的替代方案。以往研究均假设使用可听范围内的回波，但其在安静空间或禁止发声场合无法使用。本文探索使用不可听的超声波回波进行深度估计。尽管理论上超声波精度高，但实际中因易受噪声干扰和衰减影响，精度不明确。我们首先研究了高频声源限制在超声频段时的深度估计精度，发现频率受限导致精度下降。基于此，提出一种新型深度学习方法：仅在训练阶段使用可听回波作为辅助数据以提升超声波回波的深度估计性能。在公开数据集上的实验结果表明，该方法有效提高了超声波回波深度估计的准确率。
原文摘要 · Abstract (English)
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.
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