Shape matching methodologies of generic 3D objects are conventionally preceded by a pose normalization stage, that transforms objects to a canonical coordinate frame wherein feature extraction and shape matching is performed. Arguably, the canonical pose of a 3D object depends not only on its geometry but also on its semantic meaning, a characteristic that generally complicates the extraction of ground truth data. This paper introduces the first ground-truth dataset of 3D objects that allows an objective evaluation of methods which obtain the canonical pose of objects within extrinsic space. By virtue of the protocol that was followed to assemble the dataset, 3D objects of the same class share a fixed pose in terms of object center, scale and rotation while undergoing diverse shape deformations. The dataset is publicly disclosed and relevant use cases are discussed.