Metric and Relative Monocular Depth Estimation: An Overview. Fine-Tuning Depth Anything V2 👐 📚

Monocular depth estimation has significantly evolved, leading to advanced models like Depth Anything V2, which excels in predicting relative and absolute depth from single images. This approach is essential for applications in computer vision and robotics, although challenges like scale ambiguity and dataset-specific overfitting persist. The article delves into methods for fine-tuning models on custom datasets to enhance performance, emphasizing the importance of relative depth estimation and the role of innovative architectures such as Vision Transformers. It introduces a scale and shift invariant loss function for training, aiming to abstract commonalities across diverse datasets while addressing the intricacies of depth representation. The Depth Anything V2 model leverages universal training methods, DPT architecture, and synthetic data, achieving notable clarity and accuracy in depth maps. The article also offers a detailed guide on fine-tuning these models using the NYU-D dataset, highlighting the nuanced challenges and considerations in achieving robust monocular depth estimation performance.