Shape Modeling

Shape modeling focuses on representing and analyzing the geometry of objects, particularly in medical imaging and computer-aided design, aiming to capture shape variability within populations or across different instances. Current research emphasizes developing robust methods that handle noisy or incomplete data, often employing deep learning architectures like implicit neural networks, Gaussian processes, and variational autoencoders, to improve shape reconstruction and segmentation accuracy. These advancements are crucial for applications ranging from medical image analysis (e.g., organ segmentation, surgical planning) to reverse engineering and 3D modeling, enabling more accurate and efficient processing of complex shapes. The field is also actively exploring unsupervised and weakly supervised learning techniques to reduce reliance on extensive manual annotation.