Synthetic Depth

Synthetic depth generation and its application in training computer vision models is a rapidly advancing field aiming to overcome limitations of real-world data acquisition. Current research focuses on bridging the "reality gap" between synthetic and real images through techniques like color transformation and adversarial domain adaptation, often employing convolutional neural networks (CNNs) and transformers, with a particular emphasis on improving accuracy and robustness in challenging scenarios like unsupervised depth estimation and low-light conditions. This work has significant implications for various applications, including autonomous driving, robotics, and medical imaging, by enabling the creation of large, high-quality training datasets for accurate 3D scene understanding.