Skip to Main Content
In this paper, we present a quality improvement algorithm for the system, which models a human head. We have already proposed and applied the hybrid algorithm combining shape-from-silhouette with active stereo to our product of 3D head reconstruction system. Our system is characterized by the ability of the reconstruction of the whole shape and texture of the human head, even the black hair parts, while other existing systems cannot do it. Feature-based stereo algorithm adopted into our current system is the fast and robust approach. However, the depth data may be sparse and the accuracy depends on how accurate the edge detection is. On the other hand, area-based stereo algorithms generally provide dense depth data and can apply subpixel estimation, but it is comparatively time-consuming and may be inaccurate influenced by the difference of how to reflect the object in each image. To overcome these problems for improving our system practicably, we propose a novel two-stage stereo algorithm. In our algorithm, first, we adopt feature-based approach to get the depth data robustly. Next, an interpolation of the depth data is performed to predict the depth data at the unestimated pixels on the edge. Finally, applying area-based approach and subpixel estimation refine the depth data. In this paper, we describe our hybrid modeling algorithm and two-stage stereo algorithm with some experimental results.
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Volume:3 )
Date of Conference: 23-26 Aug. 2004