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| RI | Thesis Oral | 1 Apr 2008 | |
Robotics Institute Thesis Oral 1 Apr 2008
Place and Time |
Abstract |
Further Details |
Thesis Committee
Statistical Approaches to Multi-scale Point Cloud Processing
Ranjith Unnikrishnan
Robotics Institute
Carnegie Mellon University
| Place and Time |
NSH 1507
2:30 PM
| Abstract |
In recent years, 3D geometry has gained increasing popularity as the new
form of digital media content. Due to advances in sensor technology, it
is now feasible to acquire highly detailed 3D scans of complex scenes to
obtain millions of data points at high sampling rates over large spatial
extents. This ability to acquire high-resolution depth information
brings with it the possibility of using 3D geometric data to construct
detailed shape models and of perhaps combining 3D depth with visual
appearance from images to address challenging problems in computer vision.
However, geometric information represented as a 3D point cloud presents
challenges uniquely different from other data modalities such as images
or audio. Due to a combination of reasons such as the spatial
irregularity of the data and the implicit nature of 3D observations, an
easy substitution of traditional signal processing operators from images
for processing unorganized 3D points is not possible. Furthermore,
traditional estimators from classical statistics are not suitable for
processing data in this domain, and new algorithms as well as different
criteria for evaluating these algorithms are necessary.
This dissertation contributes towards the development of two fundamental
building blocks for processing point clouds. The first is of geometric
model fitting, where we present a class of locally semi-parametric
estimators that allows finite-sample analysis of accuracy and also
explicitly addresses the problem of support-radius selection in local
fitting. The second is of multi-scale filtering operators for point
clouds that allow detection of interest regions whose locations as well
as spatial extent are completely data-driven. The proposed approaches
are distinguished from related work by operating directly in the input
3D space on unorganized points without assuming an available mesh or
resorting to an intermediate global 2D parameterization.
Results are presented for several applications including surface
reconstruction, accurate shape descriptor computation and repeatable
interest region detection, on synthetic data, as well as outdoor aerial
and ground-based data obtained with a laser scanner.
| Further Details |
A copy of the thesis oral document can be found at http://www.cs.cmu.edu/~ranjith/thesis.pdf.
| Thesis Committee |
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