基于超声回波估计室内场景深度图
Estimating Indoor Scene Depth Maps from Ultrasonic Echoes
AI总结：
针对可听回波在禁声场景不可用的问题，本文分析超声频段深度估计精度下降现象，提出训练时以可听回波为辅助的深度学习方法，并在公开数据集上验证其精度提升。
摘要：
测量室内场景的三维几何结构需要专用的深度传感器，而这类传感器并不总是可得。基于回波的深度估计近来已被研究为一种有前景的替代方案。此前所有研究都假设使用可听频率范围内的回波。然而，一个主要问题是，在安静空间或其他禁止发出可听声音的情形下，无法使用可听回波。本文考虑利用人耳听不见的超声回波进行基于回波的深度估计。尽管超声波在理论上可提供很高的测量精度，但由于其对噪声敏感且易受衰减影响，使用超声回波时的实际深度估计精度仍不明确。我们首先研究了当声源频率被限制在高频段时的深度估计精度，发现当频率被限制在超声范围时，精度会下降。基于这一观察，我们提出一种新的深度学习方法，通过仅在训练阶段将可听回波用作辅助数据，来提升基于超声回波的深度估计精度。在一个公开数据集上的实验结果表明，我们的方法提高了估计精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
