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Infrared thermography converts infrared radiation to infrared images and allows the object surface temperature to be measured through radiometric calibration. However, infrared images lack depth information and cannot accurately represent the surface temperature distribution of three-dimensional (3D) objects. At the same time, the temperature measurement results of an infrared thermal imager can be affected by changes in the directional emissivity of the object surface, making it difficult to guarantee the accuracy of temperature measurement. 3D imaging is commonly used to obtain the surface morphology of an object to provide reliable 3D data for subsequent research. The paper uses structured light 3D imaging and infrared thermography to reconstruct the 3D temperature field and investigates the influence of directional emissivity on temperature measurement results. Specifically, the point cloud is obtained using the structured light 3D imaging algorithm based on Gray-code encoding. The reconstructed 3D point cloud is then fused with an infrared image for 3D temperature field reconstruction. Based on the in-depth analysis of the influence of the object surface directional emissivity on the temperature measurement results, the temperature measurement error caused by the change in directional emissivity is corrected by using the geometric shape information of the object surface. The experimental results indicate that the proposed 3D temperature field reconstruction method, which is based on the fusion of structured light imaging and infrared thermography, improves the accuracy of temperature measurement results based on an effective reconstruction of the 3D temperature field. This method has important application value in various fields such as disease assessment, component processing, and instrument testing.
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