Correspondence between three-dimensional ear depth information derived from two-dimensional images and magnetic resonance imaging: Use of a neural-network model.

There is much interest in anthropometric-derived head-related transfer functions (HRTFs) for simulating audio for virtual-reality systems. Three-dimensional (3D) anthropometric measures can be measured directly from individuals, or indirectly simulated from two-dimensional (2D) pinna images. The latter often requires additional pinna, head and/or torso measures. This study investigated accuracy with which 3D depth information can be obtained solely from 2D pinna images using an unsupervised monocular-depth estimation neural-network model. Output was compared to depth information obtained from corresponding magnetic resonance imaging (MRI) head scans (ground truth). Results show that 3D depth estimates obtained from 2D pinna images corresponded closely with MRI head-scan depth values.