Fusion of thermal images and point clouds for enhanced wall temperature uniformity analysis in building environments

• Fuses thermal images with 3D point clouds for enhanced indoor wall analysis. • Provides accurate visualization and analysis of indoor wall temperatures. • Validates the full workflow in a real setting, proving feasibility and reliability. • Offers insights to improve insulation and optimize building climate control. Achieving uniform temperature distribution is essential for optimizing insulation and improving building climate control. In this study, we propose a multi-sensor digitalization approach that fuses indoor thermal images with 3D point cloud data to create a detailed, geometry-aligned visualization of wall temperatures. By mapping 2D thermal information onto an accurately calibrated 3D model, we capture fine-grained temperature variations across interior surfaces. We validate the feasibility of this workflow through a comprehensive case study that details data acquisition, calibration, and fusion procedures. Our classroom case study demonstrated that the thermal point cloud reliably maps wall temperatures across a range of approximately 15 to 35 ∘ C, with an average residual misalignment of 2.44 pixels horizontally and 3.36 pixels vertically. Furthermore, it effectively identifies localized areas of thermal non-uniformity-particularly near windows and doors-which can inform targeted insulation improvements and climate control strategies. These results offer clear guidance for practical insulation adjustments and climate control strategies and provide a robust foundation for developing digital twin models. Ultimately, our approach offers a holistic view of the building thermal environment, leading to enhanced occupant comfort and increased energy efficiency.