## 1 Introduction  
Beamforming is a method of processing array signals and has mature applications in many fields, such as sonar, mobile communications, medical imaging, and radio astronomy [1, 2, 3]. [PERSON] and [PERSON] applied this method to sound source localization [4]. The beamforming method based on the acoustic array can locate steady-state and transient sound sources at medium and long distances with medium and high frequencies [5]. The beamforming method has been widely used in mechanical structures such as airplanes [6], trains [7], and automobiles [8]. The acoustic array-based beamforming is to locate the sound source by reconstructing the sound field in the 2D or 3D space [9]. The usual method is to assume the depth \(Z\) of the plane where the sound source is located, then perform gridding on this plane (focus reconstruction surface) and calculate the sound field output of each grid point (reconstruction point) to complete the sound field reconstruction of the plane. This continuously iterates planes of different depths to achieve the sound field reconstruction in the spatial region. The grid point (reconstruction point) where the maximum output is located is the position of the sound source. This method is limited by the number of reconstruction points and requires huge computation. Introducing the three-dimensional information of the object to be recognized and reducing the sound field reconstruction range is an effective way to reduce the amount of computation and achieve rapid positioning of the three-dimensional sound source. [PERSON] et al. [10] used the car's three-dimensional model as the sound field reconstruction area, which avoided the sound field reconstruction of the area outside the car body, and reduced the range. The above methods have the advantages of fewer reconstruction points and fast speed for sound source recognition, but the model information needs to be input into the computer in advance. Limited by the static characteristics of the model, it is difficult to identify the sound source in motion. In addition to obtaining structural information by importing the model, [PERSON] [11] uses structured light scanning technology to enhance acoustic beamforming. In the experiment, the structure information of a fixed plexiglass plate is obtained and the sound source inserted into it is located, and this proves that the acoustic imaging error is an order of magnitude lower than that of a 2D plane. Some scholars have tried to combine beamforming with vision. For example, [PERSON] et al. [12] and [PERSON] et al. [13] used a camera to dynamically track the face of the speaker, and only searched in the face area to improve the recognition accuracy of beamforming. [PERSON] et al. [14] used visually guided beamformer to improve listening in complex acoustic environments. [PERSON] et al. [15] used visual tracking on the robot and combined beamforming to locate the voice source. [PERSON] et al. [16] developed a real-time audio camera to more intuitively check the relationship between acoustic features and architectural details. In recent years, the rapid development of binocular cameras has made this an effective way to obtain 3D information in space. It has the advantages of fast measuring 3D information, high accuracy, mature technology, and continuous output of scene depth data [17; 18]. Combining the binocular camera and beamforming technology can obtain the three-dimensional surface information of the operating mechanical structure, and complete the surface sound field reconstruction of the mechanism. The radiated sound power is an important parameter to describe the acoustic characteristics [19; 20]. For example, mechanical failure will produce noise, and accurate measurement of its radiation noise can obtain the degree of failure. The sound intensity scaling method estimates sound power, which is accurate and fast under the condition of obtaining the depth of the sound source [21].