Hierarchical Superquadric Decomposition with Implicit Space Separation
This work addresses 3D reconstruction for computer vision applications, but it is incremental as it builds on prior hierarchical methods with a novel splitting technique.
The paper tackles 3D object reconstruction by hierarchically decomposing objects into superquadrics, using a new implicit space splitting method based on predicted primitives, and reports reasonable reconstructions on ShapeNet for diverse, complex geometries.
We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of superquadrics recovering finer and finer details. While such hierarchical methods have been studied before, we introduce a new way of splitting the object space using only properties of the predicted superquadrics. The method is trained and evaluated on the ShapeNet dataset. The results of our experiments suggest that reasonable reconstructions can be obtained with the proposed approach for a diverse set of objects with complex geometry.