Du, DongZhu, HemingNie, YinyuHan, XiaoguangCui, ShuguangYu, YizhouLiu, LigangBenes, Bedrich and Hauser, Helwig2021-02-272021-02-2720211467-8659https://doi.org/10.1111/cgf.14184https://diglib.eg.org:443/handle/10.1111/cgf14184Modeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure and thus has been researched for decades. However, modelling a man‐made shape with complex structures remains challenging. Existing methods adopt advanced deep learning techniques to map holistic sketches to 3D shapes. They are still bottlenecked to deal with complicated topologies. In this paper, we decouple the task of sketch2shape into a part generation module and a part assembling module, where deep learning methods are leveraged for the implementation of both modules. By changing the focus from holistic shapes to individual parts, it eases the learning process of the shape generator and guarantees high‐quality outputs. With the learned automated part assembler, users only need a little manual tuning to obtain a desired layout. Extensive experiments and user studies demonstrate the usefulness of our proposed system.modelling interfacesmodellingpart assembly10.1111/cgf.14184222-233