SCurV: A 3D descriptor for object classification
3D Object recognition is one of the big problems in Computer Vision which has a direct impact in Robotics. There have been great advances in the last decade thanks to point cloud descriptors. These descriptors do very well at recognizing object instances in a wide variety of situations. Of great interest is also to know how descriptors perform in object classification tasks. With that idea in mind, we introduce a descriptor designed for the representation of object classes. Our descriptor, named SCurV, exploits 3D shape information and is inspired by recent findings from neurophysiology. We compute and incorporate surface curvatures and distributions of local surface point projections that represent flatness, concavity and convexity in a 3D object-centered and view-dependent descriptor. These different sources of information are combined in a novel and simple, yet effective, way of combining different features to improve classification results which can be extended to the combination of any type of descriptor. Our experimental setup compares SCurV with other recent descriptors on a large classification task. Using a large and heterogeneous database of 3D objects, we perform our experiments both on a classical, flat classification task and within a novel framework for hierarchical classification. On both tasks, the SCurV descriptor outperformed all other 3D descriptors tested.