Hosted depth estimation models

Depth estimation models infer the distance of each pixel from a camera, converting a 2D image into a dense depth map. This capability solves problems in autonomous navigation, augmented reality, industrial inspection, and 3D scene reconstruction. For example, a robot moving through an unknown warehouse uses depth to avoid obstacles; an AR app places virtual furniture on real floors by reading the depth of a room captured by a single RGB camera.

In production systems, depth models are commonly deployed as REST endpoints, accepting an image and returning a depth array or encoded map. Selection among models involves a three-way trade-off. Smaller variants such as depth-anything/Depth-Anything-V2-Small-hf or Intel/dpt-hybrid-midas favor low latency and lower memory, while larger models like depth-anything/DA3NESTED-GIANT-LARGE-1.1 or depth-anything/DA3METRIC-LARGE offer finer detail and better metric accuracy at higher compute cost. Mid-range options (e.g., LiheYoung/depth-anything-large-hf, depth-anything/Depth-Anything-V2-Large-hf, xingyang1/Distill-Any-Depth-Large-hf, depth-anything/DA3-LARGE) balance quality and throughput for most applications.

Calling a hosted API eliminates the overhead of GPU provisioning, model update management, and scaling under variable loads, making it the practical choice for all but the highest-volume, latency-critical deployments.