Non Rigid

Non-rigid registration and reconstruction focus on aligning and modeling the shapes of objects that deform, unlike rigid objects. Current research emphasizes developing robust algorithms and models, including deep learning-based approaches (e.g., diffusion models, neural implicit functions) and optimization techniques (e.g., mixed-integer programming), to handle challenges like noise, partial data, and complex deformations in various data modalities (point clouds, images, videos). These advancements are crucial for applications ranging from medical imaging and robotics to augmented reality and computer graphics, enabling more accurate 3D modeling and analysis of dynamic objects.