用CAD模型和点云数据，实现高精度表面缺陷自动检测。
Surface Defect Identification using Bayesian Filtering on a 3D Mesh
- 将CAD模型转为密集网格，以顶点为状态变量进行估计。
- 仅需约50个点云样本，即可达到亚毫米级误差。
- 适合工业质检，可融合多种传感器数据。
本文提出一种基于CAD的自动化表面缺陷检测方法。利用嵌入在CAD模型中的先验知识，并与商用立体相机和深度相机获取的点云数据相结合。首先将CAD模型转换为高密度多边形网格，每个顶点代表三维空间中的状态变量。随后采用加权最小二乘算法，基于捕获的点云测量值迭代估计工件状态。该框架具备将多种传感器信息融入CAD域的潜力，实现更全面的分析。初步结果表明，算法在感兴趣区域仅使用约50个点云样本时，即达到亚毫米级标准偏差，展现出利用商用立体相机进行高精度质量控制的前景。
原文摘要 · Abstract (English)
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.
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