MiDaS vs Depth Anything: Choosing the Right Monocular Depth Model for Robotics

MiDaS excels at producing visually consistent, high-quality relative depth maps because of its training on a massive, mixed dataset of 3D movies and stereo pairs. For example, in standard benchmarks like the zero-shot cross-dataset transfer test, MiDaS v3.1 achieves state-of-the-art accuracy for relative depth, making it a reliable backbone for tasks like novel view synthesis or background defocus where the absolute scale is irrelevant.

Depth Anything takes a fundamentally different approach by leveraging a combination of 1.5 million labeled images and 62 million unlabeled images in a teacher-student framework. This strategy results in a model with superior zero-shot generalization for metric depth estimation—predicting actual distances in meters—without requiring in-domain camera intrinsics. The trade-off is a larger model footprint and higher computational demand for the largest variants compared to the leanest MiDaS models.

The key trade-off: If your priority is a lightweight model for fast, visually plausible relative depth on edge devices, choose MiDaS. If you require metric-scale depth for downstream robotic manipulation, grasping, or precise 3D scene reconstruction without per-camera calibration, choose Depth Anything. Consider MiDaS for artistic or visualization tasks, and Depth Anything when the depth values must drive physical actions.

MiDaS excels at generating robust, high-fidelity relative depth maps because it was trained on a massive, mixed dataset of 3D movies, stereo pairs, and synthetic scenes. For example, MiDaS v3.1 consistently produces visually sharp depth boundaries and correct ordinal relationships (which object is in front of which) even on in-the-wild internet photos, making it a reliable backbone for tasks like background defocus or initial scene layout estimation where absolute scale is irrelevant.

Depth Anything takes a fundamentally different approach by leveraging a data engine that auto-annotates 62 million unlabeled images, combined with a DINOv2 semantic backbone. This strategy results in a model that not only predicts relative depth but also generalizes to metric depth in a zero-shot manner. In robotics benchmarks, Depth Anything V2 demonstrates a lower absolute relative error (AbsRel) on unseen indoor scenes compared to MiDaS, which requires metric fine-tuning on a specific camera setup to output true distances.

The key trade-off: If your priority is a battle-tested, plug-and-play model for visual effects, asset generation, or any pipeline where pixel-wise ordinal accuracy matters more than physical units, choose MiDaS. If you are a robotics perception lead who needs a single model to provide usable metric depth estimates across diverse, unseen environments without per-camera calibration, choose Depth Anything. For downstream manipulation tasks where grasping points depend on real-world coordinates, Depth Anything's zero-shot metric capability provides a more direct path to deployment.