Optimal Coherent Point Selection for 3D Quality Inspection from Silhouette-Based Reconstructions

3D Geometric quality inspection involves assessing and comparing a reconstructed object to a predefined reference model or design that defines its expected volume. Achieving precise 3D object geometry reconstruction from multiple views can be challenging. In this research, we propose a camera-coherent point selection method to measure differences with the reference. The result is a point cloud extracted from the reconstruction that represents the best-case scenario, ensuring that any deviations from the reference are represented as seen from the cameras. This algorithm has been tested in both simulated and real conditions, reducing reconstruction errors by up to one fifth compared to traditional 3D reconstruction methodologies. Furthermore, this strategy assures that any existing difference with its reference really exists and it is a best-case scenario. It offers a fast and robust pipeline for comprehensive 3D geometric quality assurance, contributing significantly to advancements in the field of 3D object inspection.

Automated summary

The research proposes a camera-coherent point selection method for precise measurement of differences between a reconstructed object and a reference model. The algorithm reduces reconstruction errors by up to one fifth compared to traditional 3D reconstruction methods and assures the existence of any differences with the reference. This contributes significantly to advancements in the field of 3D object inspection.