2024
Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds
ICLR 2024spotlight
We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional metho…