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Assembly-based tools provide a powerful modeling paradigm for non-expertshape designers. However, choosing a component from a large shape repositoryand aligning it to a partial assembly can become a daunting task. In this paperwe describe novel neural network architectures for suggesting complementarycomponents and their placement for an incomplete 3D part assembly. Unlike mostexisting techniques, our networks are trained on unlabeled data obtained frompublic online repositories, and do not rely on consistent part segmentations orlabels. Absence of labels poses a challenge in indexing the database of partsfor the retrieval. We address it by jointly training embedding and retrievalnetworks, where the first indexes parts by mapping them to a low-dimensionalfeature space, and the second maps partial assemblies to appropriatecomplements. The combinatorial nature of part arrangements poses anotherchallenge, since the retrieval network is not a function: several complementscan be appropriate for the same input. Thus, instead of predicting a singleoutput, we train our network to predict a probability distribution over thespace of part embeddings. This allows our method to deal with ambiguities andnaturally enables a UI that seamlessly integrates user preferences into thedesign process. We demonstrate that our method can be used to design complexshapes with minimal or no user input. To evaluate our approach we develop anovel benchmark for component suggestion systems demonstrating significantimprovement over state-of-the-art techniques.

ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling