Surface Defect Identification using Bayesian Filtering on a 3D Mesh
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917906819842048 |
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| author | Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele |
| author_facet | Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Surface Defect Identification using Bayesian Filtering on a 3D Mesh Vedove, Matteo Dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Computer Vision and Pattern Recognition Robotics 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. |
| title | Surface Defect Identification using Bayesian Filtering on a 3D Mesh |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2501.18315 |