Structured light illumination (SLI) is the process of projecting a series of light striped patterns such that, when viewed at an angle, a digital camera can reconstruct a 3-D model of a target object's surface. But by relying on a series of time multiplexed patterns, SLI is not typically associated with video applications. For this purpose of acquiring 3-D video, a common SLI technique is to drive the projector/camera pair at very high frame rates such that any object's motion is small over the pattern set. But at these high frame rates, the speed at which the incoming video can be processed becomes an issue. So much so that many video-based SLI systems record camera frames to memory and then apply off-line processing. In order to overcome this processing bottleneck and produce 3-D point clouds in real-time, we present a lookup-table (LUT) based solution that in our experiments, using a 640 by 480 video stream, can generate intermediate phase data at 1063.8 frames per second and full 3-D coordinate point clouds at 228.3 frames per second. These achievements are 25 and 10 times faster than previously reported studies. At the same time, a novel dual-frequency pattern is developed which combines a high-frequency sinusoid component with a unit-frequency sinusoid component, where the high-frequency component is used to generate robust phase information and the unit-frequency component is used to reduce phase unwrapping ambiguities. Finally, we developed a gamma model for SLI, which can correct the non-linear distortion caused by the optical devices. For three-step phase measuring profilometry (PMP), analysis of the root mean squared error of the corrected phase showed a 60х reduction in phase error when the gamma calibration is performed versus 33х reduction without calibration.
Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_diss-1084 |
Date | 01 January 2010 |
Creators | Liu, Kai |
Publisher | UKnowledge |
Source Sets | University of Kentucky |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | University of Kentucky Doctoral Dissertations |
Page generated in 0.0021 seconds