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We propose a novel technique for producing high-quality 3D models that matcha given target object image or scan. Our method is based on retrieving anexisting shape from a database of 3D models and then deforming its parts tomatch the target shape. Unlike previous approaches that independently focus oneither shape retrieval or deformation, we propose a joint learning procedurethat simultaneously trains the neural deformation module along with theembedding space used by the retrieval module. This enables our network to learna deformation-aware embedding space, so that retrieved models are more amenableto match the target after an appropriate deformation. In fact, we use theembedding space to guide the shape pairs used to train the deformation module,so that it invests its capacity in learning deformations between meaningfulshape pairs. Furthermore, our novel part-aware deformation module can work withinconsistent and diverse part-structures on the source shapes. We demonstratethe benefits of our joint training not only on our novel framework, but also onother state-of-the-art neural deformation modules proposed in recent years.Lastly, we also show that our jointly-trained method outperforms variousnon-joint baselines.

Joint Learning of 3D Shape Retrieval and Deformation