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Estimating the 6D pose of natural objects, such as vegetables and fruit, is a challenging problem due to the high variability of their shape. The shape variation limits the accuracy of previous pose estimation approaches because they assume that the training model and the object in the target scene have the exact same shape. To overcome this issue, we propose a novel framework that consists of a local and a global hypothesis generation pipeline with a mutual verification step. The new localdoi:10.1109/iccvw.2017.256 dblp:conf/iccvw/ParkPV17 fatcat:4ck52c5xnbd6da7k4gbzzumtkq