Stereo Network

Stereo networks, employing convolutional neural networks, aim to accurately estimate depth from pairs of images by leveraging the disparity between corresponding points. Current research focuses on improving robustness and generalization, addressing challenges like domain adaptation (handling differences between training and real-world data), continual learning (adapting to new scenes without forgetting old ones), and adversarial attacks. These advancements are crucial for reliable depth estimation in diverse and dynamic environments, impacting applications such as autonomous driving, augmented reality, and 3D scene reconstruction.