Ground Truth Depth

Ground truth depth, the accurate 3D distance of scene points from a camera, is crucial for training and evaluating computer vision models, particularly in depth estimation and 3D scene understanding. Current research focuses on developing robust self-supervised and semi-supervised learning methods to overcome the challenges of acquiring expensive and time-consuming ground truth depth data, often employing convolutional neural networks (CNNs) and vision transformers (ViTs) in various architectures. These advancements are driving improvements in applications like autonomous driving, robotics, and medical imaging, where accurate depth perception is essential for safe and effective operation. The development of large-scale, high-quality synthetic datasets and novel loss functions are also key areas of focus to improve model accuracy and generalization.