Early Experience of Depth Estimation on Intricate Objects using Generative Adversarial Networks

Object parts within a scene observed by the human eye exhibit their own unique depth. Producing a single image with an accurate depth of field has many implications, namely: virtual and augmented reality, mobile robotics, digital photography and medical imaging. In this work, we aim to exploit the effectiveness of conditional Generative Adversarial Networks (GAN) to improve depth estimation from a singular inexpensive monocular sensor camera sensor. The complexity of an object shape, texture and environmental conditions make depth estimations challenging. Our approach is evaluated on our novel depth map dataset we release publicly containing the challenging photo-depth image pairs. Standard evaluation metrics against other depth map estimation techniques demonstrates the effectiveness of our approach. A study of the effectiveness of GAN on different test data is demonstrated both qualitatively and quantitatively.