# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 1 Introduction  
3D reconstruction has a wide range of applications in various application areas, including digital twin, virtual reality, and engineering design. Its primary objective is to restore the 3D structure of a target object or scene. The continuous evolution of sensor technologies has made it possible to obtain high-quality and accurate point cloud and image data [1]. However, due to the rapid development of digital information and the increasing complexity of neural networks, relying solely on a single sensor is insufficient for capturing the rich features of target objects. Laser scanning techniques are widely employed in 3D reconstruction due to their ability to provide highly accurate 3D scanning data with sufficient geometric detail. However, 3D-scanned point clouds often lack color and texture information, making it challenging to capture fine details, especially in cases involving occlusion. Additionally, acquiring precise data necessitates careful planning of the scanner's measurement positions. Laser tracking systems provide high-precision 3D measurements of target reflectors with six-degree-of-freedom (6 DoF) capabilities, which can provide accurate information about the position of the target object. Nevertheless, datacollected with laser tracking systems are sparse and lack detailed surface characterization. RGB-D images acquired using cameras and depth sensors have gained widespread use in 3D vision applications. Compared with 3D point cloud data, RGB images contain rich geometric information, and combining 3D point clouds with color information allows for the extraction of comprehensive geometric and textural features [2]. However, RGB-D sensors are limited by scanning range and high noise level [3]. To overcome these limitations, multiple sensors have been employed to measure different regions and angles of the same target, resulting in the acquisition of multi-source and multi-modal data. The fusion of such data enables more accurate reconstruction of target objects. Recently, multi-modal data fusion has become a research hotspot in the field of computer vision. Its objective is to integrate various data from different sources, types, and forms to enhance data accuracy and precision, thereby facilitating more effective data utilization. Data fusion, based on multi-source and multi-modal techniques, can acquire the overall characteristics of target objects [4]. The widespread deployment of multi-modal sensors in various fields has generated large amounts of data, which are characterized by their high volume, variety, and integrity [5]. In the context of data, multi-modal refers to the presence of different forms of data representation or different formats within the same form [6]. Multi-modal data fusion is a technique that combines information obtained from multiple sensors, data sources, or different regions and angles into a consolidated presentation [7]. The integration of information from different sources reduces the uncertainty of individual data points and provides a more comprehensive and complete description of features. Recently, deep learning-based multi-modal data fusion methods have demonstrated the ability to directly map raw multi-modal data to end-to-end strategies. This approach not only simplifies the complexity of data fusion implementation, but also effectively utilizes input data from different sensors, such as depth cameras and lasers. [PERSON] et al. [8] proposed a complex environment autonomous navigation method based on a deep multi-modal fusion network that fuses three visual modalities, namely, LiDAR data, RGB images, and point clouds, to enhance the perception ability of robots in complex environments. [PERSON] et al. [9] proposed a multi-modal object recognition method that fuses multiple deep learning models, significantly improving the accuracy of object recognition. [PERSON] et al.