We introduce a new problem of retrieving 3D models that are deformable to a
given query shape and present a novel deep deformation-aware embedding to solve
this retrieval task. 3D model retrieval is a fundamental operation for
recovering a clean and complete 3D model from a noisy and partial 3D scan.
However, given a finite collection of 3D shapes, even the closest model to a
query may not be satisfactory. This motivates us to apply 3D model deformation
techniques to adapt the retrieved model so as to better fit the query. Yet,
certain restrictions are enforced in most 3D deformation techniques to preserve
important features of the original model that prevent a perfect fitting of the
deformed model to the query. This gap between the deformed model and the query
induces asymmetric relationships among the models, which cannot be handled by
typical metric learning techniques. Thus, to retrieve the best models for
fitting, we propose a novel deep embedding approach that learns the asymmetric
relationships by leveraging location-dependent egocentric distance fields. We
also propose two strategies for training the embedding network. We demonstrate
that both of these approaches outperform other baselines in our experiments
with both synthetic and real data. Our project page can be found at
this https URL

本研究提出了一种基于深度学习的变形感知嵌入方法，致力于用于解决给定查询形状的可变形三维模型检索问题，并通过实验表明，该方法比传统基于度量的方法表现更出色。