Depth Estimation
Depth Estimation
Goal: Generate an image of depths from one or two images.
Background
Depth vs. Disparity
For stereo methods, people usually estimate pixel disparity rather than depth.
That is, determining how far a pixel moves along the epipolar line between two images.
Usually, this involves first rectifying an images using RANSAC or similar. Then a cost volume can be built. Then argmin is applied to the cost volume to find the best disparity estimate.
Disparity is related to depth by the following formula: \[disparity = baseline * focal / depth\]
- \(focal\) is the focal length in pixels. This is the distance to your image in pixels.
- This can be calculated as
(height/2) * cot(fov_h/2). - You can't imagine focal length as a depth since if the image is closer then the pixel sizes will be smaller and thus the distance to the image stays the same.
- In the formula, this term acts as a correction factor for the resolution of the disparity.
- This can be calculated as
- \(baseline\) is the distance between the camera positions. This should be in the same units as your depth.
Stereo Depth
Typically people use cost-volume to estimate depth from a stereo camera setup.
- StereoNet (ECCV 2018) (My Summary) is a method by Google's Augmented Perception team.
- Casual 3D photography (SIGGRAPH ASIA 2017) includes a method for refining cost volumes and a system for synthesizing views from a few dozen photos]
Depth from Motion
Depth is generated in real-time based on motion of the camera