  The ear, as an important part of the human head, has received much less
attention compared to the human face in the area of computer vision. Inspired
by previous work on monocular 3D face reconstruction using an autoencoder
structure to achieve self-supervised learning, we aim to utilise such a
framework to tackle the 3D ear reconstruction task, where more subtle and
difficult curves and features are present on the 2D ear input images. Our Human
Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape
parameters for 3D ear meshes, without any supervision to these parameters. To
make our approach cover the variance for in-the-wild images, even grayscale
images, we propose an in-the-wild ear colour model. The constructed end-to-end
self-supervised model is then evaluated both with 2D landmark localisation
performance and the appearance of the reconstructed 3D ears.
