Depth perception is a key component for autonomous systems that interact in
the real world, such as delivery robots, warehouse robots, and self-driving
cars. Tasks in autonomous robotics such as 3D object recognition, simultaneous
localization and mapping (SLAM), path planning and navigation, require some
form of 3D spatial information. Depth perception is a long-standing research
problem in computer vision and robotics and has had a long history. Many
approaches using deep learning, ranging from structure from motion,
shape-from-X, monocular, binocular, and multi-view stereo, have yielded
acceptable results. However, there are several shortcomings of these methods
such as requiring expensive hardware, needing supervised training data, no
ground truth data for comparison, and disregard for occlusion. In order to
address these shortcomings, this work proposes a new deep convolutional
generative adversarial network architecture, called Y-GAN, that uses data from
three cameras to estimate a depth map for each frame in a multi-camera video
stream.
