We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlusion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach builds upon graph matching to find correspondences between scene and model points. The robustness is increased through a parametrization where each graph vertex represents a full rigid transformation. We speed up the matching through greedy multi-step graph pruning and a constant-time feature matching. Quantitative and qualitative experiments demonstrate that our method is robust, efficient, and able to detect rigid and non-rigid objects and exceeds state of the art.     «

We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlusion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach builds upon graph matching to find correspondences between scene and model points. The robustness is increased through a parametrization where...     »