Image-Based Motion Compensation for Structured Light Scanning of Dynamic Surfaces

Structured light scanning systems based on temporal pattern codification produce dense and robust results on static scenes but behave very poorly when applied to dynamic scenes in which objects are allowed to move or to deform during the acquisition process. The main reason for this lies in the wrong combination of encoded correspondence information because the same point in the projector pattern sequence can map to different points within the camera images due to depth changes over time. We present a novel approach suitable for measuring and compensating such kind of pattern motion. The described technique can be combined with existing active range scanning systems designed for static surface reconstruction making them applicable for the dynamic case. We demonstrate the benefits of our method by integrating it into a gray code based structured light scanner, which runs at thirty 3d scans per second

ENHANCEMENTS TO THE MODIFIED COMPOSITE PATTERN METHOD OF STRUCTURED LIGHT 3D CAPTURE

The use of structured light illumination techniques for three-dimensional data acquisition is, in many cases, limited to stationary subjects due to the multiple pattern projections needed for depth analysis. Traditional Composite Pattern (CP) multiplexing utilizes sinusoidal modulation of individual projection patterns to allow numerous patterns to be combined into a single image. However, due to demodulation artifacts, it is often difficult to accurately recover the subject surface contour information. On the other hand, if one were to project an image consisting of many thin, identical stripes onto the surface, one could, by isolating each stripe center, recreate a very accurate representation of surface contour. But in this case, recovery of depth information via triangulation would be quite difficult. The method described herein, Modified Composite Pattern (MCP), is a conjunction of these two concepts. Combining a traditional Composite Pattern multiplexed projection image with a pattern of thin stripes allows for accurate surface representation combined with non-ambiguous identification of projection pattern elements. In this way, it is possible to recover surface depth characteristics using only a single structured light projection. The technique described utilizes a binary structured light projection sequence (consisting of four unique images) modulated according to Composite Pattern methodology. A stripe pattern overlay is then applied to the pattern. Upon projection and imaging of the subject surface, the stripe pattern is isolated, and the composite pattern information demodulated and recovered, allowing for 3D surface representation. In this research, the MCP technique is considered specifically in the context of a Hidden Markov Process Model. Updated processing methodologies explained herein make use of the Viterbi algorithm for the purpose of optimal analysis of MCP encoded images. Additionally, we techniques are introduced which, when implemented, allow fully automated processing of the Modified Composite Pattern image

Structured-light based sensing using a single fixed fringe grating: Fringe boundary detection and 3-D reconstruction

Advanced electronic manufacturing requires the 3-D inspection of very small surfaces like the solder bumps on wafers for direct die-to-die bonding. Yet the microscopic size and highly specular and textureless nature of the surfaces make the task difficult. It is also demanded that the size of the entire inspection system be small so as to minimize restraint on the operation of the various moving parts involved in the manufacturing process. In this paper, we describe a new 3-D reconstruction mechanism for the task. The mechanism is based upon the well-known concept of structured-light projection, but adapted to a new configuration that owns a particularly small system size and operates in a different manner. Unlike the traditional mechanisms which involve an array of light sources that occupy a rather extended physical space, the proposed mechanism consists of only a single light source plus a binary grating for projecting binary pattern. To allow the projection at each position of the inspected surface to vary and form distinct binary code, the binary grating is shifted in space. In every shift, a separate image of the illuminated surface is taken. With the use of pattern projection, and of discrete coding instead of analog coding in the projection, issues like texture-absence, image saturation, and image noise of the inspected surfaces are much lessened. Experimental results on a variety of objects are presented to illustrate the effectiveness of this mechanism. © 2008 IEEE.published_or_final_versio

3D Shape Measurement of Objects in Motion and Objects with Complex Surfaces

This thesis aims to address the issues caused by high reflective surface and object with motion in the three dimensional (3D) shape measurement based on phase shifting profilometry (PSP). Firstly, the influence of the reflectivity of the object surface on the fringe patterns is analysed. One of the essential factors related to phase precision is modulation index, which has a direct relationship with the surface reflectivity. A comparative study focusing on the modulation index of different materials is presented. The distribution of modulation index for different material samples is statistically analysed, which leads to the conclusion that the modulation index is determined by the diffuse reflectivity. Then the method based on optimized combination of multiple reflected image patterns is proposed to address the saturation issue and improve the accuracy for the reconstruction of object with high reflectivity.A set of phase shifted sinusoidal fringe patterns with different exposure time are projected to the object and then captured by camera. Then a set of masks are generated to select the data for the compositing. Maximalsignal-to-noise ratio combining model is employed to form the composite images pattern. The composite images are then used to phase mapping.Comparing to the method only using the highest intensity of pixels for compositing image, the signal noise ratio (SNR) of composite image is increased due to more efficient use of information carried by the images