Mesh Segmentation

Mesh segmentation aims to partition 3D meshes into meaningful parts, a crucial task with applications in diverse fields like medicine and computer graphics. Current research focuses on developing efficient and accurate segmentation methods, employing architectures such as autoregressive transformers, graph message-passing networks, and convolutional neural networks often incorporating techniques like multi-view rendering, novel mesh tokenization strategies, and self-supervised learning to improve performance and reduce reliance on labeled data. These advancements are improving the accuracy and speed of mesh segmentation, enabling better analysis of complex 3D shapes in various applications, including biomedical image analysis and 3D model generation.