Composite Shape Modeling via Latent Space Factorization
This work addresses the challenge of part-level manipulation in 3D shapes for applications in computer graphics and design, representing an incremental advancement over existing methods.
The authors tackled the problem of semantic structure-aware 3D shape modeling by developing a neural network architecture called Decomposer-Composer, which enables part-level shape manipulation through a factorized embedding space and explicit part deformation, resulting in improved performance as demonstrated in ablation studies and comparisons.
We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and produces a novel factorized shape embedding space, where the semantic structure of the shape collection translates into a data-dependent sub-space factorization, and where shape composition and decomposition become simple linear operations on the embedding coordinates. We further propose to model shape assembly using an explicit learned part deformation module, which utilizes a 3D spatial transformer network to perform an in-network volumetric grid deformation, and which allows us to train the whole system end-to-end. The resulting network allows us to perform part-level shape manipulation, unattainable by existing approaches. Our extensive ablation study, comparison to baseline methods and qualitative analysis demonstrate the improved performance of the proposed method.