The 3DCOMPAT++ dataset offers a rich collection of stylized 3D shapes with fine-grained part annotations, material information, and hierarchical semantic levels. It introduces a new task, Grounded CoMPaT Recognition (GCR), challenging researchers to recognize and ground compositions of materials on parts of 3D objects. The dataset enables shape classification, part segmentation, material tagging, and shape generation tasks.
The content discusses the importance of material information in enhancing object understanding tasks and highlights the limitations of existing datasets in providing part-level annotations. It details the data collection pipeline, rendering process, and tools provided to support users in utilizing the dataset effectively.
Key metrics and figures are presented to evaluate the performance of various models on shape classification, part segmentation tasks in both fine-grained and coarse-grained levels. The GCR challenge results showcase different baselines' performance on recognizing and grounding part-material pairs in shapes.
Overall, the 3DCOMPAT++ dataset serves as a valuable resource for advancing research in compositional 3D visual understanding through its detailed annotations and multimodal learning capabilities.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Habib Slim,X... at arxiv.org 03-13-2024
https://arxiv.org/pdf/2310.18511.pdfDeeper Inquiries