Abstract—A novel method is proposed in this paper for automatic acquisition of three-dimensional models of unknown objects by an active vision system, in which the vision sensor is to be moved from one viewpoint to the next around the target to obtain its complete model. In each step, sensing parameters are determined automatically for incrementally building the 3D target models. The method is developed by analyzing the target’s trend surface, which is the regional feature of a surface for describing the global tendency of change. Whilst previous approaches to trend analysis are usually focused on generating polynomial equations for interpreting regression surfaces in three dimensions, this paper proposes a new mathematical model for predicting the unknown area of the object surface. A uniform surface model is established by analyzing the surface curvatures. Furthermore, a criterion is defined to determine the exploration direction and an algorithm is developed for determining the parameters of the next view. Implementation of the method is carried out to validate the proposed method. Index Terms—vision sensor, viewpoint planning, sensor placement, 3D modeling, trend surface, surface prediction, model acquisition. U I.

Abstract—Structured light vision systems have been successfully used for accurate measurement of 3-D surfaces in computer vision. However, their applications are mainly limited to scanning stationary objects so far since tens of images have to be captured for recovering one 3-D scene. This paper presents an idea for real-time acquisition of 3-D surface data by a specially coded vision system. To achieve 3-D measurement for a dynamic scene, the data acquisition must be performed with only a single image. A principle of uniquely color-encoded pattern projection is proposed to design a color matrix for improving the reconstruction efficiency. The matrix is produced by a special code sequence and a number of state transitions. A color projector is controlled by a computer to generate the desired color patterns in the scene. The unique indexing of the light codes is crucial here for color projection since it is essential that each light grid be uniquely identified by incorporating local neighborhoods so that 3-D reconstruction can be performed with only local analysis of a single image. A scheme is presented to describe such a vision processing method for fast 3-D data acquisition. Practical experimental performance is provided to analyze the efficiency of the proposed methods. Index Terms—Color-encoded, computer vision, perception, realtime measurement, robotics, structured-light, 3-D data acquisition,

Abstract — A calibration method for a structured light system by observing a planar object from unknown viewpoints is proposed. A structured light system captures a 3D shape by a camera that observes a light stripe on an object illuminated by a projector. The 3D shape, obtained from the system defined by a pinhole model for the projection of a light stripe, is solved using the equation of a plane model for the projector. The coefficients of each light stripe’s equation are estimated using the 4×3 image-to-camera transformation matrix that is expressed by camera parameters. Experimental results demonstrate a high degree of accuracy when following the proposed approach. I.