Depth Supervision

Depth supervision in computer vision involves using depth information, either real or estimated, to improve the accuracy and robustness of various tasks, primarily aiming to overcome limitations of relying solely on 2D image data. Current research focuses on integrating depth supervision into diverse architectures, including neural radiance fields (NeRFs), transformers, and convolutional neural networks (CNNs), often employing techniques like optimal transport or self-supervised learning to handle noisy or uncertain depth estimates. This approach significantly enhances performance in applications such as 3D object detection, novel view synthesis, and scene reconstruction, leading to more accurate and detailed 3D representations from limited input data.