Search Results for: computational stereo

Given two images of a scene taken from slightly different viewpoints, a stereo image pair, it’s possible to determine the disparity for every pixel using template matching. The disparity image is one where the value of each pixel is inversely related to the distance between that point in the scene and the camera.

Let’s recap the important points from the topics we have covered about human depth perception, display of 3D images and estimating 3D scene structure using stereo and other types of sensors.

Vision is useful to us and to almost all forms of life on the planet, perhaps robots could do more if they could also see. Robots could mimic human stereo vision or use cameras with superhuman capability such as wide angle or panoramic views.

The relationship between world coordinates, image coordinates and camera spatial velocity has some interesting ramifications. Some very different camera motions cause identical motion of points in the image, and some camera motions leads to no change in the image at all in some parts of the image. Let’s explore at these phenomena and how we […]

One very powerful trick used by humans is binocular vision. The images from each eye are quite similar, but there is a small horizontal shift, a disparity, between them and that shift is a function of the object distance.

Humans have long been fascinated with seeing images and movies in ‘3D’. Let’s look at how human stereo vision works and some of the technologies used to present images to our eyes in ‘3D’.

In order to determine the size and distance of objects in the scene our brain uses a number of highly evolved tricks. Let’s look at some of these.

An image is a two dimensional projection of a three dimensional world. The big problem with this projection is that big distant objects appear the same size as small close objects. For people, and robots, it’s important to distinguish these different situations. Let’s look at how humans and robots can determine the scale of objects […]

We previously learnt how to derive a Jacobian which relates the velocity of a point, defined relative to one coordinate frame, to the velocity relative to a different coordinate frame. Now we extend that to the 3D case.

Let’s look at some recent research results that vividly show how information from many 2D images taken from many different locations can be combined to form a detailed 3D model of the world.