High-speed structured light scanning system and 3D gestural point cloud recognition

In computer-vision-based human computer interaction (HCI), higher-quality signal leads to better system performance. In this paper, we develop a real-time high-resolution 3D object scanning system based on structured light illumination (SLI). Our system fuses depth information with RGB texture to reconstruct high-resolution 3D point cloud. The point cloud preserves accurate surface geometry of the object (e.g., finger postures of hands, facial expressions, etc). Respectively, for a 640 × 480 video stream, our system can generate phase and texture video at 1500 frames per second (fps) and produce full 3D point clouds at 300 fps. For gesture recognition, we propose to combine the module of robust face recognition with the module of 3D point cloud classification. Moreover, rather than extracting sophisticated features, we leverage the accurate reconstruction and classify each point cloud by directly matching the whole 3D surface geometry with the templates of different classes. The proposed recognition system is robust to the scaling, translation, rotation and texture of objects. Finally, utilizing the system, we contribute to the research community two large-scale high-resolution 3D point cloud databases, i.e., SLI 3D Hand Gesture Database and SLI 3D Face Database. The proposed point cloud recognition approach achieves recognition rates up to 98.0% over the gesture database and 88.2% over the face database in our pilot study.