Abstract
Inductive Logic Programming (ILP) is used to learn classifiers for generic object recognition from range images and 3D point clouds. The point cloud is segmented into primitive regions, followed by labelling subsets of regions representing an object. Predicates describing those regions and their relationships are constructed and used for learning. Using planar regions as the only primitive shape was examined in previous work. We extend this by adding two more primitives: cylinders and spheres. We compare the performance of learning with the planar-only method using some common household objects. The results show that the additional primitives reduce the number of features required to describe an instance and also significantly reduce the learning time without loss in accuracy.
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