Auto-calibration method for a projector-camera system

TL;DR: In this paper, a system and method for calibrating a plurality of projectors for 3D scene reconstruction is described, including a camera, a camera-projector calibration module and a projector-projectors calibration module.

TL;DR: In this article, a display parameter is computed for a video projector, the display parameter being computed as a function of second calibration parameter, and a sub-image to be projected by the video projector is calculated according to the computed display parameter.

TL;DR: In this article, a calibration method of a line-structured light vision measuring sensor based on the space plane restriction, comprising of the following calibrating steps: placing a two-dimensional planar target in a camera field range, collecting a group of images of multiple characteristic circles at different positions on a 2D target panel, determining the target topological relation, and synchronously completing the internal parameter calibration and the light planar calibration of a camera.

TL;DR: An image processing apparatus and method for calibrating a depth sensor can be found in this article, where a depth image of a target object captured by a depth camera and a color image of the target object capturing by a color camera are used to calibrate the depth sensor.

TL;DR: It is shown that the pose estimation problem can be formulated as that of minimizing an error metric based on collinearity in object (as opposed to image) space, and an iterative algorithm which directly computes orthogonal rotation matrices and which is globally convergent is derived.

TL;DR: It is shown that pose ambiguities - two distinct local minima of the according error function - exist even for cases with wide angle lenses and close range targets, and a new algorithm for unique and robust pose estimation from a planar target is developed.

TL;DR: In this paper, it was shown that the optic flow field arising from motion relative to a visually textured plane may be characterized by eight parameters that depend on the observer's linear and angular velocity and the coordinate vector of the plane.

TL;DR: Based on four coplanar correspondences of two externally uncalibrated cameras, 3D reconstruction can be achieved in Euclidean space with only one unknown scaling parameter as mentioned in this paper.