How to Estimate Depth from a Single Image

Monocular depth estimation (MDE) is a critical task in computer vision, enabling depth prediction from a single image, which is essential for applications such as autonomous driving and robotics. This process is challenging due to the inherent ambiguity in projecting 3D scenes onto 2D images, requiring the consideration of cues like object size and perspective. The article explores the use of Hugging Face and FiftyOne for running and evaluating MDE models using the SUN-RGBD dataset. It highlights the use of transformer-based models like DPT and diffusion-based models such as Marigold, emphasizing the importance of visualizing depth maps beyond relying solely on evaluation metrics like RMSE, PSNR, and SSIM. The challenges of MDE, including data quality and the limitations of metrics in assessing model performance, are discussed, underscoring the necessity of a qualitative assessment of model predictions.