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RESEARCH PAPERS

Model and Algorithms for Point Cloud Construction Using Digital Projection Patterns

[+] Author and Article Information
Tao Peng

Department of Mechanical Engineering, University of Maryland, College Park, MD 20742pengtao@umd.edu

Satyandra K. Gupta

Department of Mechanical Engineering and Institute for Systems Research University of Maryland, College Park, MD 20742skgupta@eng.umd.edu

J. Comput. Inf. Sci. Eng 7(4), 372-381 (Jul 29, 2007) (10 pages) doi:10.1115/1.2798115 History: Received November 20, 2006; Revised July 29, 2007

This paper describes a computational framework for constructing point clouds using digital projection patterns. The basic principle behind the approach is to project known patterns on the object using a digital projector. A digital camera is then used to take images of the object with the known projection patterns imposed on it. Due to the presence of 3D faces of the object, the projection patterns appear distorted in the images. The images are analyzed to construct the 3D point cloud that is capable of introducing the observed distortions in the images. The approach described in this paper presents three advances over the previously developed approaches. First, it is capable of working with the projection patterns that have variable fringe widths and curved fringes and hence can provide improved accuracy. Second, our algorithm minimizes the number of images needed for creating the 3D point cloud. Finally, we use a hybrid approach that uses a combination of reference plane images and estimated system parameters to construct the point cloud. This approach provides good run-time computational performance and simplifies the system calibration.

Copyright © 2007 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Schematic diagram of a PCCDFP system with one projector and one camera

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Figure 2

Measuring a cylinder using different fringe patterns: fixed fringe width versus variable fringe width; (a) sinusoidal fringe pattern (fixed fringe width), (b) projection pattern with variable fringe width, (c) image under the sinusoidal fringe pattern shown in (a), and (d) image under the variable width fringe pattern shown in (b)

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Figure 3

Pinhole camera model

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Figure 4

Mathematical model for the PCCDFP system

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Figure 5

A set of eight (generalized) fringe patterns used in phase map construction

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Figure 6

Construction of point cloud

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Figure 7

Find the pixel coordinates of NI in the reference phase map ΦR(u,v)

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Figure 8

Estimation of the projector’s projection center

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Figure 9

Acquisition of the absolute phase maps ΦV(u,v) and ΦH(u,v) by using vertical and horizontal fringe patterns, respectively; (a) one of the images for acquiring ΦV, (b) contours of the phase map ΦV, (c) one of the images for acquiring ΦH, and (d) contours of the phase map ΦH

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Figure 10

Measurement of a spline surface; (a) picture of the spline surface, (b) automatically generated adaptive projection pattern, and (c) measure performance adaptive pattern versus fixed-pitch fringe patterns

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Figure 11

Measurement of a plastic flower pot; (a) photograph of the flower pot, (b) automatically generated adaptive projection pattern, (c) rendering of the point cloud acquired, (d) enlarged view of a mesh rendering of the point cloud, and (e) measurement coverage: adaptive pattern versus fixed-pitch fringe patterns

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Figure 12

Measurement of a plastic drill housing; (a) photograph of the drill housing and (b) rendering of the point cloud acquired

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Figure 13

Measurement of the base of a telephone; (a) photograph of the telephone and (b) rendering of the point cloud acquired

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Figure 14

Measurement of gauge part 1; (a) photograph of the gauge part, (b) the acquired point cloud after being aligned with the CAD model of the part, and (c) distribution of the divergence between the point cloud and the CAD model

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Figure 15

Measurement of gauge part 2; (a) photograph of the gauge part and (b) rendering of the point cloud acquired

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