This article introduces an innovative visual coarseregistration process suitable for textureless objects. Because our framework is industrial, the process is designed for metallic, complex objects containing multiple bores and repetitive patterns.

This technique is based on a local shape descriptor, invariant under affine transform, 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 viewsphere 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 between two descriptors. This ability is exploited to obtain efficiently a great number of coarse pose hypothesis. A pose hypothesis classification method is proposed to select the best-ones.

An evaluation on a cylinder head and a binding beam confirms both the robustness and the precision of the process.