. Our generative model jointly analyzes the structure and geometry of shapes, encoding them into a single latent code. The highlighted triplets above demonstrate that, in this joint latent space, pairs of nearby points represent models that are close to each other in both geometry and structure, while stepping away from the pair introduces differences in either geometry, or structure, or both. We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure) are jointly learned and embedded in a latent space by an autoencoder. The encoder intertwines the geometry and structure features into a single latent code, while the decoder disentangles the features and reconstructs the geometry and structure of the 3D model. Our autoencoder consists of two branches, one for the structure and one for the geometry. The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code. This explicit intertwining of information enables separately controlling the geometry and the structure of the generated models. We evaluate the performance of our method and conduct an ablation study. We explicitly show that encoding of shapes accounts for both similarities in structure and geometry. A variety of quality results generated by SAGNet are presented.
REF utilize an autoencoder of two branches to encode geometry features and structure features separately, and fuse them into a single latent code to intertwine the two types of features for shape modeling.
SAGNet:Structure-aware Generative Network for 3D-Shape Modeling
ACM Transactions on Graphics (SIGGRAPH 2019) 38, 4, Article 91
Computer Science Mathematics