The research described in this dissertation focuses on the development of a new
approach for the generation of geometric models from multiple-view range image data.
Through intensive comparison and evaluation of different representations, the
cross-section contour based representation is concluded to be ideal for modeling with
range image data. The representation is shown to be at an intermediate level -
compatible with both the low-level of range image data and with the need to provide
relatively high-level geometric and topological information in models.
A new concept of generating partial models within device frames, frames
associated with the working principle and geometry of a range sensor, is introduced.
The range data are well distributed in the device frame. This good data
distribution facilitates computations relevant to rendering the cross-sections required
by the representation and relevent to identifying occlusions present in the image.
Methodology for merging the partial models with a current global model is developed
to allow the incorporation of redundancy between the partial model and the current
global model and to allow growth of the global model. A simulation of the ERIM
imaging-radar based range sensor, a prototype triangulation-based range sensor
developed for this research and a commercial HYMARC range sensing system are
used for approach verification. The device frames associated with the sensors are
derived, and used to test the modeling approaches and the developed system.
The presented research: demonstrates the suitability of the cross-section based
representation for range-image based modeling systems; introduces a new concept and
associated methods for generating cross-section contour models in range sensor device
frames to take advantage of well distributed data; develops a series of algorithms
for partial modeling in the device frame and for global model integration; and
demonstrates the feasibility of the developed new approaches for applications by
testing the system for multiple sensor types. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/9748 |
| Date | 19 July 2018 |
| Creators | Yao, Helai |
| Contributors | Podhorodeski, Ronald Peter, Dong, Zuomin |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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