Part-level single-view 3D shape reconstruction with multiple types of primitives

In recent years, various methods have been proposed for reconstructing the 3D shape of an object from a single view image. While methods that reconstruct the object as a single model show promising results, they often lack part-level details. On the other hand, part-level reconstruction methods provide recognition of parts but struggle to represent detailed shapes due to the use of a single primitive. To address this issue, this paper proposes a Compositionally Generalizable 3D Structure Prediction Network using Multiple Types of Primitives (CompNet-MTP). CompNet-MTP first estimates the parameters of each type of primitive for every part and then selects the appropriate primitive type to construct the 3D shape of the object. In the experiments, we used cylinders in addition to cuboids, which are commonly used as primitive shapes. Experimental results confirm the effectiveness of the proposed network in handling multiple types of primitives.