Deep Cascade Generation on Point Sets
This paper proposes a deep cascade network to generate 3D geometry of an object on a point cloud, consisting of a set of permutation insensitive points. Such a surface representation is easy to learn from, but inhibits exploiting rich low dimensional topological manifolds of the object shape due to lack of geometric connectivity. For benefiting from its simple structure yet utilizing rich neighborhood information across points, this paper proposes a two stage cascade model on point sets. Specifically, our method adopts the state of the art point set autoencoder to generate a sparsely coarse shape first, and then locally refines it by encoding neighborhood connectivity on a graph representation. An ensemble of sparse refined surface is designed to alleviate the suffering from local minima caused by modeling complex geometric manifolds. Moreover, our model develops a dynamically weighted loss function for jointly penalizing the generation output of cascade levels at different training stages in a coarse to fine manner. Comparative evaluation on the publicly benchmarking ShapeNet dataset demonstrates superior performance of the proposed model to the state of the art methods on both single view shape reconstruction and shape autoencoding applications.
IJCAI