New York University Depth V2

NYU Depth V2 is a benchmark dataset driving advancements in monocular depth estimation, the task of inferring 3D depth from a single 2D image. Current research focuses on improving accuracy, particularly in challenging low-texture areas, using various architectures including convolutional neural networks (CNNs), transformers, and encoder-decoder models often enhanced with techniques like knowledge distillation, adaptive loss functions, and edge-guided features. These improvements are crucial for applications such as robotics, autonomous driving, and 3D scene reconstruction, where accurate depth perception is essential. Furthermore, research explores data augmentation strategies to enhance model generalization and robustness.