Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and definitive classification of patterns for structured light sensors. We have implemented a set of representative techniques in this field and present some comparative results. The advantages and constraints of the different patterns are also discussed.

We present a survey of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information. In fact, depth perception is one of the most important subjects in computer vision. Stereovision is an attractive and widely used method, but, it is rather limited to make 3D surface maps, due to the correspondence problem. The correspondence problem can be improved using a method based on structured light concept, projecting a given pattern on the measuring surfaces. However, some relations between the projected pattern and the reflected one must be solved. This relationship can be directly found codifying the projected light, so that, each imaged region of the projected pattern carries the needed information to solve the correspondence problem.

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

We introduce a method to imperceptibly embed arbitrary binary patterns into ordinary color images displayed by unmodified off-the-shelf Digital Light Processing (DLP) projectors. The encoded images are visible only to cameras synchronized with the projectors and exposed for a short interval, while the original images appear only minimally degraded to the human eye. To achieve this goal, we analyze and exploit the micro-mirror modulation pattern used by the projection technology to generate intensity levels for each pixel and color channel. Our real-time embedding process maps the user’s original color image values to the nearest values whose camera-perceived intensities are the ones desired by the binary image to be embedded. The color differences caused by this mapping process are compensated by error-diffusion dithering. The non-intrusive nature of our novel approach allows simultaneous (immersive) display and acquisition under controlled lighting conditions, as defined on a pixel level by the binary patterns. We therefore introduce structured light techniques into human-inhabited mixed and augmented reality environments, where they previously often were too intrusive. 1.

This paper presents a new active range scanning technique suitable for moving or deformable surfaces. It is a `one-shot' system, in that 3D data are acquired from a single image. The projection pattern consists of equidistant black and white stripes combined with a limited number of colored, transversal stripes which aid in their identification. Instead of using a generic, static code that is supposed to work under all circumstances, a 'self-adaptive code' is used. Two modes of adaptation are provided. The first mode is off-line, and generates a robust identification code for the current projector-camera configuration. This configuration is supposed to remain fixed during the remainder of the 3D capturing session. The second mode occurs on-line. By introducing a feedback-loop from the reconstruction output to the pattern generation, the pattern can be adapted as to keep its decoding well-conditioned. Within the considered family of patterns, its parameters are optimized on the fly based on the current content of the scene. As a matter of fact, scene content of the current frame influences the pattern projected during the next. This poses a weak temporal continuity constraint. Only a very fast change of scene invalidates this assumption. The higher the throughput of the reconstruction pipeline, the less serious this constraint becomes. Our current pipeline is running at approximately 20 Hz. Our prototype only uses 'off the shelf' hardware.

This paper examines the construction of a 3-D surface model of an object rotating in front of a camera. Previous research in depth from motion has demonstrated the power of using an incremental approach to depth estimation. In this paper, we extend this approach to more general motion and use a full 3-D surface model instead of a 2 1 = 2 -D sketch. The algorithm starts with a flow field computed using local correlation. It then projects individual measurements into 3-D points with associated uncertainties. Nearby points from successive frames are merged to improve the position estimates. These points are then used to construct a finite element surface model, which is itself refined over time. We demonstrate the application of our new techniques to several real image sequences. Keywords: Computer vision, 3-D model construction, image sequence (motion) analysis, optic flow, Kalman filter, surface interpolation, computer aided design, computer graphics animation. c flDigital Equipment C...

The scanning of 3D geometry has become a popular way of capturing the shape of real-world objects. Transparent objects, however, pose problems for traditional scanning methods. We present a tomographic method for recovering the shape of objects made of

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In this paper an approach to real-time position correction and environmental modeling based on odometry, ultrasonic sensing, structured light sensing and active stereo vision (bin- and trinocular) is presented. Odometry provides the robot with a position estimation and with the help of a model of the environment sensor perceptions can be matched to predictions. Ultrasonic sensing is capable of collision avoidance and obstacle detection and so enables navigation in simply structured environments. Model-based image processing allows detecting and classifying natural landmarks in the stereo images uniquely. With only one observation the robot's position and orientation relative to the observed landmark is found precisely. This sensing strategy is used when high precision is necessary for the performance of the navigation task. Finally techniques are described that allow an automatic mapping of an unknown or only partially known environment. 1 Introduction The application of mobile robot...

3D profiling