3D reconstruction and precision evaluation of industrial components via Gaussian Splatting

Abstract

Traditional 3D reconstruction methods for industrial components present significant limitations. Structured light and laser scanning require costly equipment, complex procedures, and remain sensitive to scan completeness and occlusions. These constraints restrict their application in settings with budget and expertise limitations. Deep learning approaches reduce hardware requirements but fail to accurately reconstruct complex industrial surfaces with real-world data. Industrial components feature intricate geometries and surface irregularities that challenge current deep learning techniques. These methods also demand substantial computational resources, limiting industrial implementation. This paper presents a 3D reconstruction and measurement system based on Gaussian Splatting. The method incorporates adaptive modifications to address the unique surface characteristics of industrial components, ensuring both accuracy and efficiency. To resolve scale and pose discrepancies between the reconstructed Gaussian model and ground truth, a robust scaling and registration pipeline has been developed. This pipeline enables precise evaluation of reconstruction quality and measurement accuracy. Comprehensive experimental evaluations demonstrate that our approach achieves high-precision reconstruction, with an average Chamfer Distance of 2.24 and a mean F1 Score of 0.19, surpassing existing methods. Additionally, the average scale error is 2.41%. The proposed system enables reliable dimensional measurements using only consumer-grade cameras, significantly reducing equipment costs and simplifying operation, thereby improving the accessibility of 3D reconstruction in industrial applications. A publicly available industrial component dataset has been constructed to serve as a benchmark for future research. The dataset and code are available at https://github.com/ldj0o/IndustrialComponentGS.