Self-recalibration of a colour-encoded light system for automated three-dimensional measurements

Abstract

Colour-encoded structured lighting systems are widely used for three-dimensional data acquisition based on machine vision. Calibration of such a system is a laborious and tedious task. This paper presents a novel method for self-recalibration of such a vision system. The relative pose between the projector and camera of the system is automatically determined by taking a single view of the scene, so that the three-dimensional measurements and reconstruction can be performed efficiently even if it is moved from one place to another or the configuration of the system is changed. Experiments were carried out to demonstrate the implementation of the proposed method.

A whole-field 3D shape measurement algorithm based on phase-shifting projected fringe profilometry

“…Equation (5) shows that an accurate measurement result is depending on the four factors: (1) Accurate determination of the pixel coordinates on camera's image plane; 2) Unbiased camera's parameters; 3) Unbiased projector's parameters; 4) Accurate determination of the pixel point on the projector's image plane which corresponds to the pixel point on the camera's image plane. The first two conditions are easy to be satisfied with accurate camera calibration algorithm [1][2][3][4] and well-developed image processing technique [5]. However, the latter two aspects are much more difficult to be achieved.…”

“…In this process, the key to accurate reconstruction of an object's surface is proper calibration of each optical devices [5]. Many camera calibration methods are available to be utilized [1][2][3][4]. However, for projector calibration, even with the latest accurate calibration methods using active target and phase-shifting technique [3][5] [7] [10], the accuracy of projector calibration can hardly reach as the same level as the camera [5][6].…”

An Imaging Technique for Measuring Wave Surface Shapes

“…A color block pattern is projected into dye-imbued water by the PC projector, and the surface of the water is recorded by the digital camera using an experimental setup similar to that of Chen and Li (2003). Three-dimensional surface shapes are estimated on the basis of the recorded relative locations of the block projections on the surface.…”

Structured Light Methods for Underwater Imaging: Light Stripe Scanning and Photometric Stereo

“…In all cases, the search algorithm converged to the global optimum solution within several seconds. This suggests the presence of a single global minimum of the error function 4 . As a test of robustness, we added uniform random noise (up to 5% of the simulated values) and found that the errors in recovered unknowns were low, as evidenced by the error histograms in figure 10.…”

“…However, very few techniques explicitly analyze the influence of scattering on the appearances of scenes under active illumination 1 . Structured lighting is also widely used in computer vision, for 3D reconstruction of scenes [25], [14], [4], [11], [27], [19], [20]. However, an implicit assumption made in most methods is that light is neither scattered nor absorbed by the medium in which the scene and sources are immersed (as in pure air).…”