Hierarchical Grouping

Hierarchical grouping aims to organize data, such as 3D scenes or vector graphics, into nested structures reflecting different levels of granularity. Current research focuses on developing unsupervised and data-driven methods, employing architectures like recursive neural networks and clustering transformers to learn hierarchical representations from diverse data sources, including images and point clouds. These advancements improve tasks like 3D instance segmentation, object pose estimation, and semantic scene understanding, impacting fields such as robotics, augmented reality, and computer vision. The ability to robustly and efficiently represent hierarchical structures is crucial for bridging the gap between raw data and meaningful interpretations.