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Description
This paper introduces a method called Contextual Information-Based Registration (CIBR), used to accurately register large and dense point sets, which represent a 3D scene.
Distinguished from existing techniques, CIBR works with input point sets by partitioning them into discrete logical parts, which represent objects in the scene. A registration process takes place on each part of the point set, which contains the richest contextual information related to the 3D objects in the point clouds, leading to a final precise alignment.
Through experimentation, CIBR demonstrates superior precision across various datasets, with the best improvement of 267% in fitness and correspondence set size and 52.2% in inliner RMSE, even though there exist cases, when the registration was suboptimal. CIBR achieves in most cases more robust and precise registration outcomes than the traditional fine and rough ICP registration methods.
