3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes Mahmoud Ahmed Xiang Li1 Arpit Prajapati 2 Mohamed Elhoseiny

Understanding objects in 3D at the part level is essential for humans and robots to navigate and interact with the environment. Current datasets for part-level 3D object understanding encompass a limited range of categories. For instance, the ShapeNet-Part and PartNet datasets only include 16, and 24 object categories respectively. The 3DCoMPaT dataset, specifically designed for compositional understanding of parts and materials, contains only 42 object categories. To foster richer and fine-grained part-level 3D understanding, we introduce 3DCoMPaT200, a large-scale dataset tailored for compositional understanding of object parts and materials, with 200 object categories with 5 times larger object vocabulary compared to 3DCoMPaT and 4 times larger part categories.