3D Shape Measurement Using Digital Fringe Projection
Main Participants: Satyandra
K. Gupta and Tao Peng
Sponsors: This project was sponsored by MIPS, Automated
Precision Inc., and NSF
Keywords: Inspection, Reverse Engineering, and 3D Shape
Many industrial applications require accurate and rapid
measurement of the 3-D shapes of objects. Representative applications
shape measurement include reverse engineering, 3D replication,
quality control. In most of these applications, users need to construct
point clouds that correspond to the objects surface by performing
on the objects surfaces. Manufacturing industry needs a fast inspection
that can measure and analyze various 3D features on the part and
determine if a
feature is within the tolerance specifications or not. The measurement
needs to be adequately accurate to eliminate measurement errors.
errors can lead to erroneous inspection that results in an acceptable
being rejected and a defective part being accepted. Hence, both
speed and accuracy are equally important.
Coordinate measurement machines and laser
based measurement techniques usually provide very accurate
However, these techniques are slow because they measure various points
part sequentially. On the other hand, camera-based techniques are
fast. Therefore, a possible way to perform the 3D inspection is to use
cameras to construct a dense point cloud (e.g., points spaced less then
apart) corresponding to the part being inspected and then analyze the
cloud to determine if it meets the tolerance specifications. But
associated with the conventional camera based inspection techniques has
been very high in the area of measurement of geometrically complex 3D
Shape measurement based on digital fringe
projection (SMDFP) is a technique for non-contact shape measurement.
Due to its
fast speed, flexibility, low cost and potentially high accuracy, SMDFP
shown great promise in 3-D shape measurement, especially for
require acquisition of dense point clouds. A typical SMDFP system
projection unit and one or more cameras. During the shape measurement
a set of fringe patterns, whose structures are accurately controlled by
computer, are projected onto the surface of the object being measured.
Meanwhile, the images of the object shone by the light patterns are
the digital camera(s). By using image processing techniques and some
of a triangulation method, a dense 3-D point cloud representing the
the object can be constructed.
We are interested in developing a
comprehensive mathematical model for SMDFP and the associated shape
Main Results and Their Anticipated Impact
SMDFP system being used in our research utilizes a digital micro-mirror
device (DMD) to generate a projection pattern and digital camera to
take the images. This system generates an appropriate projection
pattern and uses a DMD-based projection unit to project the pattern on
the object being measured. The digital camera takes images of the
object. Due to three-dimensional nature of the object surface, the
projected pattern distorts. The images captured by camera records the
distortion in the projection pattern. Images captured by the camera are
analyzed by the system to estimate the 3D points on the object surface
that cause the distortion in the projection pattern seen in the image.
The system finally returns a 3D point cloud that represents the object
surface. DMD-based projection unit provides excellent resolution and
brightness, high contrast and color fidelity, and fast response times.
One of the key innovations behind our system is use of multiple
patterns. Different projection patterns lead to different accuracy. A
projection pattern that produces accurate result for one shape feature
may not be ideal for some other feature. Hence, different projection
patterns are needed to capture different features on the object
accurately. The use of multiple projection patterns allows the new
system to measure all the features on the object accurately. The system
also selects the projection patterns carefully to minimize the number
of patterns being used to keep the measurement process fast. Another
novel feature of the system is use of a high fidelity mathematical
model for every element of the system. This helps in improving the
overall measurement accuracy.
Our main results include:
This software generates point clouds and provides visualization
capabilities to examine the generated point clouds. This system seems
to work very well for a wide variety of shapes. A noteworthy feature of
the system is that it works extremely well with parts with holes and
discontinuities. These kind of parts posed tremendous difficulties for
vision-based technologies in past. Even for complex parts, the system
only needs to take eight images to produce very good results. Hence, it
is a very fast system. API has also done evaluation of accuracy
achieved by the system. On the test parts supplied by Ford, the system
produced average error of less than 75 microns. We believe that with
some fine-tuning we will be able to reduce this error to below 50
- Developed detailed mathematical models and functional structure
of the shape measurement system.
- Developed algorithms to estimate various system parameters.
- Developed algorithm to generate dense point clouds by analyzing
- Developed procedures for determining the appropriate projection
- Developed prototype shape measurement software.
API plans to release a commercial called 3D Rapid Scan based on our
research results. In summary, we have developed one of a kind shape
measurement system that generates dense point clouds with unprecedented
speed and accuracy for a wide variety of complex parts. This system
forms a basis for performing cheap, fast, and accurate 3D inspection
and has the potential for opening new markets for API.
The following papers provide more details on the above-described
Some of these papers are available at the publications
section of the website.
- T. Peng, S.K. Gupta, and K. Lau. Algorithms for constructing 3-D
point clouds using multiple digital fringe projection patterns. CAD Conference, Bangkok, Thailand,
- T. Peng and S.K. Gupta. Model and algorithms for point cloud
construction using digital projection patterns. ASME Journal of Computing and Information
Science in Engineering, 7(4): 372-381, 2007.
- T. Peng and S.K. Gupta. Algorithms for generating adaptive
projection patterns for 3-D shape measurement. ASME Journal of Computing and Information
Science in Engineering, 8(3), 2008.
For additional information and to obtain copies of the above papers
Dr. Satyandra K. Gupta
Department of Mechanical Engineering and Institute for Systems Research
University of Maryland
College Park, Md-20742