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Structured light is a widely employed approach for profile measurement in active stereo vision. Recent methods have developed learning-based single pattern structured light systems and unstructured light stereo vision systems. However, their methods require additional cameras or a diffractive optical element. In addition, the calibration methods of the learning-based structured light systems require a large number of images, while the stereo vision method cannot detect the blocked region. Classically, a digital projector is treated as a pseudocamera, and the stripe pattern is employed to measure the depth. However, an additional pattern is employed to calibrate the camera-projector system in the stripe-based method. In this paper, 24 reliable features and a novel calibration algorithm are proposed. With the proposed calibration algorithm, the stripe patterns are calibrated without additional patterns, and with the proposed RGB features, the number of encoded columns is enlarged significantly. Experiments demonstrate that the proposed method is accurate and robust. [ABSTRACT FROM AUTHOR]

To improve the calibration efficiency of the structured light stereo vision system, a closed-form single-pose calibration method is proposed. Previous works calibrate the structured light stereo vision system via the planar-based method since the world coordinates of the projected pixels are easy to find. But the disadvantage of these methods is that multiple poses are required, so that the mechanical movement is necessary in these calibration methods. In this paper, the inherent geometry between the spheres and their image is exploited deeply. Thus, a sphere-based calibration method is proposed. The proposed method first calibrates the camera from the image of the absolute conic through an improved algorithm. Then, the three-dimensional coordinates of the projected pixels are found via the inherent geometrical relation. Next, the projector is calibrated using the direct linear method. Finally, the distortion of the projector is found through the nonlinear optimization method with an improved updating direction. Experiments demonstrate that not only the calibration efficiency is improved, but also the calibration accuracy is improved. [ABSTRACT FROM AUTHOR]

This paper presents a novel structured light approach for the 3D reconstruction of specular surface. The binary shifting strip is adopted as structured light pattern instead of conventional sinusoidal pattern. Based on the framework of conventional High Dynamic Range (HDR) imaging technique, an efficient means is first introduced to estimate the camera response function. And then, dynamic range of the generated radiance map is compressed in the gradient domain by introducing an attenuation function. Subject to the change of lighting conditions caused by projecting different structured light patterns, the structure light image with middle exposure level is selected as the reference image and used for the slight adjustment of the primary fused image. Finally, the regenerated structured light images with well exposing condition are used for 3D reconstruction of the specular surface. To evaluate performance of the method, some stainless stamping parts with strong reflectivity are used for the experiments. And the results showed that, different specular targets with various shapes can be precisely reconstructed by the proposed method. [ABSTRACT FROM AUTHOR]

Turntable-based 3D scanners are popular but require calibration of the turntable axis. Existing methods for turntable calibration typically make use of specially designed tools, such as a chessboard or criterion sphere, which users must manually install and dismount. In this article, the authors propose an automatic method to calibrate the turntable axis without any calibration tools. Given a scan sequence of the input object, they first recover the initial rotation axis from an automatic registration step. Then they apply an iterative procedure to obtain the optimized turntable axis. This iterative procedure alternates between two steps: refining the initial pose of the input scans and approximating the rotation matrix. The performance of the proposed method was evaluated on a structured light-based scanning system. [ABSTRACT FROM AUTHOR]

3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions. A new approach directly extracts valley-ridge features from point-cloud-based 3D fingerprint models. It first applies the moving least-squares method to fit a local paraboloid surface and represent the local point cloud area. It then computes the local surface's curvatures and curvature tensors to facilitate detection of the potential valley and ridge points. The approach projects those points to the most likely valley-ridge lines, using statistical means such as covariance analysis and cross correlation. To finally extract the valley-ridge lines, it grows the polylines that approximate the projected feature points and removes the perturbations between the sampled points. Experiments with different 3D fingerprint models demonstrate this approach's feasibility and performance. [ABSTRACT FROM AUTHOR]

Three-dimensional measurement of shiny or reflective surface is a challenging issue for optical-based instrumentations. In this paper, we present a novel structured light approach for direct measurement of shiny target so as to skip the coating preprocedure. In comparison with traditional image-intensity-based structured light coding strategies like sinusoidal and line patterns, strip edges not raw image intensities are encoded in the illuminated patterns. With strip edges generally better preserved than individual image intensity in the image data in the presence of surface reflections, such a coding strategy is more robust. To remove the periodic ambiguity within strip patterns, traditional Gray code patterns are adopted. To localize the strip edges more precisely, both positive and negative strip patterns are used. An improved zero-crossing feature detector that has subpixel accuracy is proposed for strip-edge localization. The experimental setup is configured with merely an off-the-shelf pico-projector and a camera. Extensive experiments including accuracy evaluation, comparison with previous structured light algorithms, and the reconstruction of some real shiny objects are shown to demonstrate the system's accuracy and endurance against reflective nature of surfaces. [ABSTRACT FROM PUBLISHER]