  In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160
million rendered views of more than 10 million stylized 3D shapes carefully
annotated at the part-instance level, alongside matching RGB point clouds, 3D
textured meshes, depth maps, and segmentation masks. 3DCoMPaT$^{++}$ covers 41
shape categories, 275 fine-grained part categories, and 293 fine-grained
material classes that can be compositionally applied to parts of 3D objects. We
render a subset of one million stylized shapes from four equally spaced views
as well as four randomized views, leading to a total of 160 million renderings.
Parts are segmented at the instance level, with coarse-grained and fine-grained
semantic levels. We introduce a new task, called Grounded CoMPaT Recognition
(GCR), to collectively recognize and ground compositions of materials on parts
of 3D objects. Additionally, we report the outcomes of a data challenge
organized at CVPR2023, showcasing the winning method's utilization of a
modified PointNet$^{++}$ model trained on 6D inputs, and exploring alternative
techniques for GCR enhancement. We hope our work will help ease future research
on compositional 3D Vision.
