Dual Contouring

Dual contouring is a mesh reconstruction technique aiming to efficiently and accurately create 3D surface models from various data sources, such as distance fields or point clouds. Recent research focuses on improving the accuracy and robustness of dual contouring through deep learning approaches, including neural networks that predict vertex locations and edge crossings, and self-supervised learning methods that reduce reliance on manually labeled data. These advancements are significantly impacting medical image analysis, enabling automated segmentation of complex anatomical structures for applications like radiotherapy planning and improving the speed and accuracy of 3D model generation across various scientific domains.