CAD Model Visual Registration from Closed-Contour Neighborhood Descriptors
This article introduces an innovative visual registration pro-cess suitable for textureless objects. Because our framework is industrial, the process is designed for metallic, complex free-form objects containing multiple bores.
This technique is based on a new contour descriptor, invariant under affine transformation, which characterizes the neighborhood of a closed contour. The affine invariance is exploited in the learning stage to produce a lightweight model : for an automobile cylinder head, a learning view-sphere with twelve viewpoints is sufficient.
Moreover, during the learning stage, this descriptor is combined to a 2D/3D pattern, concept likewise presented in this article. Once associated, the 2D/3D information wealth of this descriptor allows a pose estimation from a single match. This ability is exploited in the registration process to drastically reduce the complexity of the algorithm and increase efficiently its robustness to the difficult problem of repetitive patterns.
Evaluations on a cylinder head, a car door and a binding beam confirm both the robustness and the precision (about 3 pixel of mean reprojection error on the full model reprojection area) of the process.
KeywordsAugmented Reality Cylinder Head Closed Contour Learning Stage Repetitive Pattern
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