The 3D measurement is widely required in modern industries. In this letter, a method based on the RGBD saliency detection with depth range adjusting (RGBD-DRA) is proposed for 3D measurement. By using superpixels and prior maps, RGBD saliency detection is utilized to detect and measure the target object automatically Meanwhile, the proposed depth range adjusting is processing while measuring to prompt the measuring accuracy further. The experimental results demonstrate the proposed method automatic and accurate, with 3 mm and 3.77% maximum deviation value and rate, respectively.

This paper proposes a three-dimensional (3D) face recognition system using passive stereo vision. So far, the reported 3D face recognition techniques have used active 3D measurement methods to capture high-quality 3D facial information. However, active methods employ structured illumination (structure projection, phase shift, moire topography, etc.) or laser scanning, which is not desirable in many human recognition applications. Addressing this problem, we propose a face recognition system that uses (i) passive stereo vision to capture 3D facial information and (ii) 3D matching using an ICP (Iterative Closest Point) algorithm with its improvement techniques. Experimental evaluation demonstrates efficient recognition performance of the proposed system compared with an active 3D face recognition system and a passive 3D face recognition system employing the original ICP algorithm.

This paper presents a high-accuracy 3D (three-dimen-sional) measurement system using multi-camera passive stereo vision to reconstruct 3D surfaces of free form objects. The proposed system is based on an efficient stereo correspondence technique, which consists of (i) coarse-to-fine correspondence search, and (ii) outlier detection and correction, both employing phase-based image matching. The proposed sub-pixel correspondence search technique contributes to dense reconstruction of arbitrary-shaped 3D surfaces with high accuracy. The outlier detection and correction technique contributes to high reliability of reconstructed 3D points. Through a set of experiments, we show that the proposed system measures 3D surfaces of objects with sub-mm accuracy. Also, we demonstrate high-quality dense 3D reconstruction of a human face as a typical example of free form objects. The result suggests a potential possibility of our approach to be used in many computer vision applications.

In this paper, a new method for measuring three-dimensional (3D) moving facial shapes is introduced. This method uses two light sources and a slit pattern projector. First, the normal vectors at points on a face are computed by the photometric stereo method with two light sources and a conventional video camera. Next, multiple light stripes are projected onto the face with a slit pattern projector. The 3D coordinates of the points on the stripes are measured using the stereo vision algorithm. The normal vectors are then integrated within 2D finite intervals around the measured points on the stripes. The 3D curved segment within each finite interval is computed by the integration. Finally, all the curved segments are blended into the complete facial shape using a family of exponential functions. By switching the light rays at high speed, the time required for sampling data can be reduced, and the 3D shape of a moving human face at each instant can be measured.