In this figure, the world point \(P=(x,z)\) is projected into the left image as \(p_l\) and into the right image as \(p_r\).īy defining right triangles we can find two similar triangles: with vertices at \((0,0)-(0,z)-(x,z)\) and \((0,0)-(0,f)-(f,x_l)\). Here the Y-axis is perpendicular to the page. Two cameras L and R are separated by a baseline b. Practical details: classical stereo, 80s/90s.Ĭonsider a simple model of stereo vision: we have two cameras whose optic axes are parallel.
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Now, knowing geometric relationships, how to build stereo systems. There are some scale factors (which we get from calibration), simple reciprocal/inverse relationship 2 points will lie on the same row in both images. The x-coordinate is the only difference, one is translated by the baseline $B$. Our goal is to “reverse” this 3d to 2d projection. If we send out two rays from a 3D scene point to the two camera centers, a triangle is constructed in the process. The matching process is the key computation in stereo.Ĭonsider the geometry of a standard, “narrow-baseline” stereo rig. The correspondence problem is defined as finding a match across these two images to determine, What is the part of the scene on the right that matches that location? Previously, the community used only search techniques and optimization to solve this problem. However, if a large part of the scene is seen in both images, two pixel will correspond to the same structure (3D point) in the real world.
Notably, there will be a per pixel shift in the scene as a consequence of the camera’s new, different position in 3d space. Think of two cameras collecting images at the same time. However, in other cases, we may need to calibrate them constantly, if cameras are constantly moving with respect to one another. If we fix the cameras’ position relative to each other, we can calibrate just once and operate under a known relationship. Keep in mind: the camera could be moving. It allows us to understand shape of world, and what is in the image. It is the key to unlocking the images and using them for 3d reasoning. Depth is crucial for navigating in the world. Depth is the missing ingredient, and the goal of computer vision here is to recover the missing depth information of the scene when and where the image was acquired. The basic idea is the following: a camera takes picture of the same scene, in two different positions, and we wish to recover the depth at each pixel.
Humans also use stereo vision, with a baseline (distance between our eyes) of 60 mm. The task requires finding matching pixels in the two images and converting the 2D positions of these matches into 3D depths.
“Stereo matching” is the task of estimating a 3D model of a scene from two or more images.
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