Shape Datasets

Shape datasets are crucial for training machine learning models to understand and manipulate 3D shapes, addressing challenges in areas like computer-aided design, robotics, and biological modeling. Current research focuses on developing robust and efficient methods for representing and learning from shape data, including the use of implicit neural representations, graph transformers for mesh segmentation, and self-supervised learning techniques to disentangle shape content from transformations. These advancements aim to overcome limitations posed by data scarcity, noisy data, and the need for generalization across diverse shape types, ultimately improving the accuracy and applicability of 3D shape analysis in various scientific and engineering domains.