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 https://deformscan2cad.github.io/.

中文翻译：

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变形感知 3D 模型嵌入和检索

我们引入了检索可变形为给定查询形状的 3D 模型的新问题，并提出了一种新颖的深度变形感知嵌入来解决此检索任务。3D 模型检索是从嘈杂和部分 3D 扫描中恢复干净完整 3D 模型的基本操作。然而，给定有限的 3D 形状集合，即使是最接近查询的模型也可能无法令人满意。这促使我们应用 3D 模型变形技术来调整检索到的模型，以便更好地拟合查询。然而，在大多数 3D 变形技术中强制执行某些限制以保留原始模型的重要特征，这些特征阻止变形模型与查询的完美拟合。变形模型和查询之间的这种差距导致模型之间的不对称关系，这是典型的度量学习技术无法处理的。因此，为了检索拟合的最佳模型，我们提出了一种新颖的深度嵌入方法，该方法通过利用与位置相关的以自我为中心的距离场来学习不对称关系。我们还提出了两种训练嵌入网络的策略。我们证明，在我们使用合成数据和真实数据的实验中，这两种方法都优于其他基线。我们的项目页面可以在 https://deformscan2cad.github.io/ 找到。我们证明，在我们使用合成数据和真实数据的实验中，这两种方法都优于其他基线。我们的项目页面可以在 https://deformscan2cad.github.io/ 找到。我们证明，在我们使用合成数据和真实数据的实验中，这两种方法都优于其他基线。我们的项目页面可以在 https://deformscan2cad.github.io/ 找到。