Issue No. 03 - March (2012 vol. 34)
Yongchang Wang , Univ. of Kentucky, Lexington, KY, USA
Kai Liu , Univ. of Kentucky, Lexington, KY, USA
Qi Hao , Univ. of Alabama, Tuscaloosa, AL, USA
Xianwang Wang , Hewlett-Packard (HP) Labs., Cupertino, CA, USA
D. L. Lau , Univ. of Kentucky, Lexington, KY, USA
L. G. Hassebrook , Univ. of Kentucky, Lexington, KY, USA
Active stereo vision is a method of 3D surface scanning involving the projecting and capturing of a series of light patterns where depth is derived from correspondences between the observed and projected patterns. In contrast, passive stereo vision reveals depth through correspondences between textured images from two or more cameras. By employing a projector, active stereo vision systems find correspondences between two or more cameras, without ambiguity, independent of object texture. In this paper, we present a hybrid 3D reconstruction framework that supplements projected pattern correspondence matching with texture information. The proposed scheme consists of using projected pattern data to derive initial correspondences across cameras and then using texture data to eliminate ambiguities. Pattern modulation data are then used to estimate error models from which Kullback-Leibler divergence refinement is applied to reduce misregistration errors. Using only a small number of patterns, the presented approach reduces measurement errors versus traditional structured light and phase matching methodologies while being insensitive to gamma distortion, projector flickering, and secondary reflections. Experimental results demonstrate these advantages in terms of enhanced 3D reconstruction performance in the presence of noise, deterministic distortions, and conditions of texture and depth contrast.
Cameras, Stereo vision, Three dimensional displays, Noise, Reflection, Calibration, Frequency conversion
Yongchang Wang, Kai Liu, Qi Hao, Xianwang Wang, D. L. Lau and L. G. Hassebrook, "Robust Active Stereo Vision Using Kullback-Leibler Divergence," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. 3, pp. 548-563, 2012.