Combining relative and metric depth refers to a technique that integrates both relative depth information and metric depth information to improve depth estimation.
Relative Depth: Relative depth refers to the ordering of objects in a scene based on their distances from the camera. It provides information about the depth ordering but not the actual metric distances. Relative depth can be estimated using techniques such as monocular depth estimation or stereo vision.
Metric Depth: Metric depth refers to the actual distance of objects from the camera, typically measured in real-world units like meters or feet. It provides precise depth information but may require calibration and additional sensors.
By incorporating relative depth information, the algorithm can capture the depth ordering of objects in the scene. At the same time, by incorporating metric depth information, the algorithm can obtain accurate distance measurements.
One approach to combining relative and metric depth is through the use of deep learning techniques. For example, the ZoeDepth algorithm combines relative and metric depth estimation by training a neural network to predict both relative depth and metric depth from a single image. This allows the algorithm to capture the depth ordering of objects while also providing accurate depth measurements.