PAPER TITLE Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks AUTHORS Despoina Paschalidou , Angelos Katharopoulos, Andreas Geiger, Sanja Fidler ABSTRACT Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representation...