arXiv:2409.03336v1 Announce Type: cross
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.
Echo-Based Depth Estimation Using Inaudible Ultrasonic Echoes: A Multi-Disciplinary Approach
Echo-based depth estimation has gained attention in recent years as an alternative solution for measuring 3D geometric structures of indoor scenes in situations where dedicated depth sensors are not available. While previous studies on this topic have focused on echoes in the audible range, this research aims to explore the use of inaudible ultrasonic echoes. This approach opens up new possibilities for depth estimation in quiet spaces or environments where producing audible sounds is prohibited.
One key challenge faced by researchers is determining the accuracy of depth estimation when using ultrasonic echoes. Ultrasonic waves theoretically provide high measurement accuracy, but their effectiveness in practice has been unclear due to their sensitivity to noise and susceptibility to attenuation. To address this issue, the authors of this paper conducted a comprehensive investigation of depth estimation accuracy using restricted high-frequency ultrasonic waves.
The results of the investigation revealed that the accuracy of depth estimation decreased when the frequency was limited to the ultrasonic range. This finding highlights the need for innovative approaches to improve the performance of ultrasonic echo-based depth estimation. In response, the authors propose a novel deep learning method that leverages audible echoes as auxiliary data during training to enhance the accuracy of ultrasonic echo-based depth estimation.
The multi-disciplinary nature of this research is evident in various aspects. Firstly, it combines concepts from the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By exploring the potential of inaudible ultrasonic echoes, this research expands the scope of multimedia technologies by introducing a new method for depth estimation. The findings of this study have implications for the development of multimedia applications that incorporate depth sensing capabilities.
Furthermore, the adoption of a deep learning approach demonstrates the integration of artificial intelligence techniques into the field of depth estimation. This fusion of disciplines allows for the development of more accurate and robust depth estimation methods. As deep learning continues to advance, it has the potential to revolutionize the field of multimedia information systems by enabling more sophisticated and adaptive algorithms.
In conclusion, this paper presents a comprehensive study on echo-based depth estimation using inaudible ultrasonic echoes. By addressing the limitations of previous studies, the authors propose a deep learning method that leverages audible echoes during training to improve the accuracy of ultrasonic echo-based depth estimation. The findings of this research contribute to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities by introducing a new method for depth estimation and showcasing the potential of deep learning in this domain.