RGBD Infant Head Reconstruction for Cranial Vault Asymmetry Estimation

Abstract

We present an RGBD infant head reconstruction method with a mobile phone depth sensor on a novel dataset. Acquiring 3D models from infants enables many important medical tasks such as automatic cranial asymmetry classification for plagiocephaly therapy progress estimation. Existing methods for 3D infant head reconstruction employ synchronized multi-view configurations or hand-held laser scanning methods making their widespread employment difficult. In contrast, RGBD reconstruction methods either rely on static scenes failing on this task due to rapid infant head movements or employ dynamic methods lacking the high fidelity surface reconstructions required for accurate cranial measurements. We propose a domain-specific 3D reconstruction method augmenting static RGBD methods focusing on the rigid parts of the head and exploiting scene knowledge about the data acquisition methodology. We evaluate our approach using provided ground truth anthropometric measurements of the biparietal diameter and report competitive accuracy.