Surface Defect Identification using Bayesian Filtering on a 3D Mesh

TL;DR

This paper introduces a CAD-based method that uses Bayesian filtering on 3D meshes to automatically detect surface defects, integrating sensor data with CAD models for high-precision quality control.

Contribution

It presents a novel framework combining CAD models with point cloud data using Bayesian filtering, enabling accurate defect detection with minimal sensor samples.

Findings

Achieves sub-millimeter accuracy with about 50 point cloud samples

Integrates diverse sensor data into CAD domain for comprehensive analysis

Demonstrates promising defect detection performance

Abstract

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications.