DARWIN: Deformable Patient Avatar Representation With Deep Image Network

In this paper, we present a technical approach to robustly estimate the detailed patient body surface mesh under clothing cover from a single snapshot of a range sensor. Existing methods either lack level of detail of the estimated patient body model, fail to estimate the body model robustly under clothing cover, or lack sufficient evaluation over real patient datasets. In this work, we overcome these limitations by learning deep convolutional networks over real clinical dataset with large variation and augmentation. Our approach is validated with experiments conducted over 1063 human subjects from 3 different hospitals and surface errors are measured against groundtruth from CT data.