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

该研究提出了一种基于神经变形、形状检索和嵌入空间的联合学习方法来生产高质量的 3D 模型，该方法不仅能提高变形后的匹配精度，而且还能处理不一致和多样的结构形状。