Free-Viewpoint Visual Inspection via 3D Gaussian Splatting for Direct Template Matching

Kenta Ito, Shiori Ueda, Shohei Mori, Junichi Sugano, Hideyuki Adachi, Hideo Saito

Abstract: Machine vision systems play a pivotal role in streamlining manufacturing processes, notably in quality control through automatic in-line visual inspections. A common practice for inspecting parts, components, and final products is to use a master part benchmark for quality comparison. However, challenges arise when objects enter inspection points in unintended orientations. This misalignment potentially leads to erroneous decisions by automated systems, resulting in additional checkpoints or wastage affecting the production rate. To tackle this issue, we propose a visual inspection pipeline that leverages recent machine learning-based approaches to compare the inspection target and a master part virtually oriented to the same perspective. Specifically, we suggest combining 3D Gaussian Splatting and DUSt3R as a practical solution. Our approach demonstrates its efficacy in real-world scenarios through testing on three mock parts and a real industrial component.

External IDs:dblp:conf/iecon/ItoUMSAS24