Image: Mo et al
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Ranked #2 on 3D Object Classification on ModelNet40
We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.
3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface.
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications.
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #4 on 3D Object Recognition on ModelNet40
To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.