: Data driven synthesis of 3D objects. (Left) Given an input collection of oriented surface points from an object class we learn a low-dimensional shape embedding using a deep probabilistic auto-encoder. (Middle) Samples from the prior embedding distribution can be passed through the decoder to obtain novel shape examples complete with point orientations (not shown). (Right) Smooth meshes are constructed by making use of the sampled point orientations. We introduce a generative model of part-segmented 3D objects: the shape variational auto-encoder (ShapeVAE). The ShapeVAE describes a joint distribution over the existence of object parts, the locations of a dense set of surface points, and over surface normals associated with these points. Our model makes use of a deep encoder-decoder architecture that leverages the partdecomposability of 3D objects to embed high-dimensional shape representations and sample novel instances. Given an input collection of part-segmented objects with dense point correspondences the ShapeVAE is capable of synthesizing novel, realistic shapes, and by performing conditional inference enables imputation of missing parts or surface normals. In addition, by generating both points and surface normals, our model allows for the use of powerful surface-reconstruction methods for mesh synthesis. We provide a quantitative evaluation of the ShapeVAE on shape-completion and test-set log-likelihood tasks and demonstrate that the model performs favourably against strong baselines. We demonstrate qualitatively that the ShapeVAE produces plausible shape samples, and that it captures a semantically meaningful shape-embedding. In addition we show that the ShapeVAE facilitates mesh reconstruction by sampling consistent surface normals.
Nash and Williams REF propose a ShapeVAE to generate segmented 3D objects and the model is trained using shapes with dense point correspondence.
The shape variational autoencoder: A deep generative model of part-segmented 3D objects
Comput. Graph. Forum
Comput. Graph. Forum
Computer Science