Title of article
Surround structured lighting: 3-D scanning with orthographic illumination
Author/Authors
Lanman، نويسنده , , Douglas and Crispell، نويسنده , , Daniel and Taubin، نويسنده , , Gabriel، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2009
Pages
11
From page
1107
To page
1117
Abstract
This paper presents a new system for rapidly acquiring complete 3-D surface models using a single orthographic structured light projector, a pair of planar mirrors, and one or more synchronized cameras. Using the mirrors, we project structured light patterns that illuminate the object from all sides (not just the side of the projector) and are able to observe the object from several vantage points simultaneously. This system requires that projected planes of light to be parallel, so we construct an orthographic projector using a Fresnel lens and a commercial DLP projector. A single Gray code sequence is used to encode a set of vertically-spaced light planes within the scanning volume, and five views of the illuminated object are obtained from a single image of the planar mirrors located behind it. From each real and virtual camera we recover a dense 3-D point cloud spanning the entire object surface using traditional structured light algorithms. A key benefit of this design is to ensure that each point on the object surface can be assigned an unambiguous Gray code sequence, despite the possibility of being illuminated from multiple directions. In addition to presenting a prototype implementation, we also develop a complete set of mechanical alignment and calibration procedures for utilizing orthographic projectors in computer vision applications. As we demonstrate, the proposed system overcomes a major hurdle to achieving full 360° reconstructions using a single structured light sequence by eliminating the need for merging multiple scans or multiplexing several projectors.
Keywords
3-D reconstruction , Full object scanning , Gray codes , Orthographic projection , Structured lighting
Journal title
Computer Vision and Image Understanding
Serial Year
2009
Journal title
Computer Vision and Image Understanding
Record number
1695694
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