As the use of simulation increases across many diff erent application domains,
the need for high- fidelity three-dimensional virtual representations of real-world environments
has never been greater. This need has driven the research and development
of both faster and easier methodologies for creating such representations. In this research,
we present two diff erent inference-based geometric modeling techniques that
support the automatic construction of complex cluttered environments.
The fi rst method we present is a surface reconstruction-based approach that
is capable of reconstructing solid models from a point cloud capture of a cluttered
environment. Our algorithm is capable of identifying objects of interest amongst a
cluttered scene, and reconstructing complete representations of these objects even in
the presence of occluded surfaces. This approach incorporates a predictive modeling
framework that uses a set of user provided models for prior knowledge, and applies
this knowledge to the iterative identifi cation and construction process. Our approach
uses a local to global construction process guided by rules for fi tting high quality
surface patches obtained from these prior models. We demonstrate the application of
this algorithm on several synthetic and real-world datasets containing heavy clutter and occlusion.
The second method we present is a generative modeling-based approach that can
construct a wide variety of diverse models based on user provided templates. This
technique leverages an inference-based construction algorithm for developing solid
models from these template objects. This algorithm samples and extracts surface
patches from the input models, and develops a Petri net structure that is used by our
algorithm for properly fitting these patches in a consistent fashion. Our approach uses
this generated structure, along with a defi ned parameterization (either user-defi ned
through a simple sketch-based interface or algorithmically de fined through various
methods), to automatically construct objects of varying sizes and con figurations.
These variations can include arbitrary articulation, and repetition and interchanging
of parts sampled from the input models.
Finally, we affim our motivation by showing an application of these two approaches.
We demonstrate how the constructed environments can be easily used
within a physically-based simulation, capable of supporting many diff erent application
domains.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2011-05-9209 |
| Date | 2011 May 1900 |
| Creators | Biggers, Keith Edward |
| Contributors | Keyser, John, Williams, Glen |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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