In 3D reconstruction tasks, single-sensor data fusion based on deep learning is limited by the integrity and accuracy of the data, which reduces the accuracy and reliability of the fusion results. To address this issue, this study proposes a multi-sensor data fusion method based on a self-attention mechanism. A multi-sensor data fusion model for acquiring multi-source and multi-modal data is constructed, with the core component being a convolutional neural network with self-attention (CNN-SA), which employs CNNs to process multi-source and multi-modal data by extracting their features. Additionally, it introduces an SA mechanism to weigh and sum the features of different modalities, adaptively focusing on the importance of different modal data. This enables mutual support, complementarity, and correction among the multi-modal data. Experimental results demonstrate that the accuracy of the CNN-SA network is improved by 72.6%, surpassing the improvements of 29.9% for CNN-CBAM, 23.6% for CNN, and 11.4% for CNN-LSTM, exhibiting enhanced generalization capability, accuracy, and robustness. The proposed approach will contribute to the effectiveness of multi-sensor data fusion processing.

In complex measurement systems, scanning the shape data of solid models is time consuming, and real-time solutions are required. Therefore, we developed a 3D non-rigid transformation deduction model based on multi-control point perception data. We combined a convolutional neural network (CNN), gated recurrent unit (GRU), and self-attention mechanism (SA) to develop the CNN-GRU-SA deduction network, which can deduce 3D non-rigid transformations based on multiple control points. We compared the proposed network to several other networks, with the experimental results indicating that the maximum improvements in terms of loss and root-mean-squared error (RMSE) on the training set were 39% and 49%, respectively; the corresponding values for the testing set were 48% and 29%. Moreover, the average deviation of the inference results and average inference time were 0.55 mm and 0.021 s, respectively. Hence, the proposed deep learning method provides an effective method to simulate and deduce the 3D non-rigid transformation processes of entities in the measurement system space, thus highlighting its practical significance in optimizing entity deformation.

In multi-source fusion deformation-monitoring methods that utilize fiber Bragg grating (FBG) data and other data types, the lack of FBG constraint points in edge regions often results in inaccuracies in fusion results, thereby impacting the overall deformation-monitoring accuracy. This study proposes a multi-source fusion deformation-monitoring calibration method and develops a calibration model that integrates vision and FBG multi-source fusion data. The core of this model is a normal distribution transform (NDT)–convolutional neural network (CNN)–self-attention (SA) calibration network. This network enhances continuity between points in point clouds using the NDT module, thereby reducing outliers at the edges of the fusion results. Experimental validation shows that this method reduces the absolute error to below 0.2 mm between multi-source fusion calibration results and high-precision measured point clouds, with a confidence interval of 99%. The NDT-CNN-SA network offers significant advantages, with a performance improvement of 36.57% over the CNN network, 14.39% over the CNN–gated recurrent unit (GRU)–convolutional block attention module (CBAM) network, and 9.54% over the CNN–long short term memory (LSTM)–SA network, thereby demonstrating its superior generalization, accuracy, and robustness. This calibration method provides smoother and accurate structural deformation data, supports real-time deformation monitoring, and reduces the impact of assembly deviation on product quality and performance.