Explore the Best Depth Estimation Models

Depth estimation is a computer vision task that predicts the distance between the camera and objects in an image, resulting in a depth map that is crucial for applications like autonomous driving and augmented reality. Several models are explored, including Depth Anything V2, DepthCrafter, MiDaS, Depth Pro, Marigold, and FoundationStereo, each with unique strengths and weaknesses. Depth Anything V2 is noted for its efficiency and accuracy, particularly in complex scenes, while DepthCrafter excels in video depth estimation with temporal consistency. MiDaS shows strong cross-dataset transferability, and Depth Pro offers high-resolution metric depth maps without requiring camera intrinsics. Marigold, leveraging pretrained generative models, excels in fine detail but is relatively slower, whereas FoundationStereo extends zero-shot capabilities to stereo depth estimation. The article also discusses how to implement these models using Roboflow Workflows for tasks such as object detection and depth estimation, highlighting Depth Anything V2 for its balance of speed and accuracy.