# Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images

## TL;DR

Y-GAN introduces a novel deep generative adversarial network architecture that estimates depth maps from multi-camera stereo images, addressing hardware, data, and occlusion limitations in existing depth perception methods for autonomous systems.

## Contribution

The paper presents Y-GAN, a new GAN-based model that leverages three-camera data to improve depth estimation in autonomous robotics.

## Key findings

- Y-GAN effectively estimates depth maps from multi-camera inputs.
- The approach addresses hardware and data limitations of previous methods.
- Y-GAN handles occlusion issues in depth perception.

## Abstract

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.