Robust method in photogrammetric reconstruction of geometric
primitives in solid modeling.
Ph.D. Thesis Xiang Yang
The 3D point cloud is a widely used data format obtained from
scanning a 3D model, either
by using active 3D laser range scanners or passive photogrammetric
methods. Since the topological
information in a point cloud is captured on 3D point level, the
inverse design cannot be carried out directly on the data.
The point cloud is first segmented into various geometric
primitives, such as planes, spheres and cylinders, then the
modification and redesign of solid
model can be more easily achieved.
A robust estimator is required to detect the multiple inlier
structures while filtering out the
outliers. In this dissertation, we present a new robust algorithm
which processes each structure
independently. The user gives only the number of elemental subsets
for random sampling,
which is also required in other robust algorithms. This method
provides a general solution
of robust estimation, and no tuning of other parameters are required
for particular estimation
tasks. The scales of the structures (tolerance of error) are
estimated adaptively and no threshold
is involved in spite of different objective functions. After
classifying all the input data, the
segmented structures are sorted by their strengths and the strongest
inlier structures come out
at the top. Like any robust estimators, this algorithm also has
limitations which are described
To illustrate its efficiency and robustness, the algorithm is tested
on various synthetic and
real examples in both 2D and 3D. We extend its applications through
the entire process of the
structure from motion method, to reconstruct the 3D point cloud from
a sequence of 2D images.
We automatically estimate and fit the 3D surfaces from the 3D point
samples, without generating
surface normals or mesh model. The designer can interact with the 3D
and direct modification of point cloud becomes applicable.
The thesis contains 100 pages.
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