Unsupervised discovery of repetitive objects
Jiwon Shin, Rudolph Triebel, Roland Siegwart · 2010
We present a novel approach for unsupervised discovery of repetitive objects from 3D point clouds. Our method assumes that objects are non-deformable and uses multiple occurrences of an object as the evidence for its existence. We segment input range data by superpixel segmentation and extract features for each segment. We search for a group of segments where each segment matches a segment in another group using a joint compatibility test. The discovered objects are then verified by the Iterative Closest Point algorithm to remove false matches. The presented method was tested on real data of complex objects. The experiments demonstrate that the proposed approach is capable of finding objects that occur multiple times in a scene and distinguish apart those objects of different types.