3D Modeling of Interior Building Environments and Objects from Noisy Sensor Suites
Eric Turner
EECS Department, University of California, Berkeley
Technical Report No. UCB/EECS-2015-105
May 14, 2015
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-105.pdf
In this dissertation, we present several techniques used to automatically generate virtual models of indoor building environments. The interior environment of a building is scanned by a custom hardware system, which provides raw laser and camera sensor readings used to develop these models. Our modeling techniques can be separated into three categories: 2D floor plan models, simplified 2.5D extruded models, and fully complex and detailed 3D models. All models are produced automatically from the output data of a backpack-mounted ambulatory scanning system, which can scan multiple floors of a building efficiently. We can capture the entirety of a large building in only a few hours, including staircases and other hard to reach locations, which improves upon the state-of-the-art static scanning by several orders of magnitude. Our algorithms are capable of producing many kinds of 3D virtual building models while traversing through a GPS-denied environment, including point clouds, 2D floor plans, and 3D building models.
The novel contributions of this dissertation fall into three groups. First, we present multiple methods for producing 2D floor plans in a scalable fashion. We automatically partition these floor plans into separate building levels and separate rooms within each level. These floor plans can also be extruded into simplified 2.5D models of the building environment. Second, we present multiple techniques to produce complex 3D models of the scanned environment, capturing all observed detail. Third, we present several techniques that combine these two types of building models -- simple and complex -- to perform additional analysis of the building environment. Such analysis includes segmenting objects and furniture in the environment as separate models, reducing noise and artifacts within the models, and demonstrating novel visualization techniques. Additionally, we show several example datasets generated by the system in real-world environments, including buildings of over 40,000 square feet. We demonstrate how such building models are applicable in many fields of study, including architecture, building energy efficiency, virtual walk-throughs of buildings, indoor navigation, and augmented and virtual reality.
Advisors: Avideh Zakhor
BibTeX citation:
@phdthesis{Turner:EECS-2015-105,
Author= {Turner, Eric},
Title= {3D Modeling of Interior Building Environments and Objects from Noisy Sensor Suites},
School= {EECS Department, University of California, Berkeley},
Year= {2015},
Month= {May},
Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-105.html},
Number= {UCB/EECS-2015-105},
Abstract= {In this dissertation, we present several techniques used to automatically generate virtual models of indoor building environments. The interior environment of a building is scanned by a custom hardware system, which provides raw laser and camera sensor readings used to develop these models. Our modeling techniques can be separated into three categories: 2D floor plan models, simplified 2.5D extruded models, and fully complex and detailed 3D models. All models are produced automatically from the output data of a backpack-mounted ambulatory scanning system, which can scan multiple floors of a building efficiently. We can capture the entirety of a large building in only a few hours, including staircases and other hard to reach locations, which improves upon the state-of-the-art static scanning by several orders of magnitude. Our algorithms are capable of producing many kinds of 3D virtual building models while traversing through a GPS-denied environment, including point clouds, 2D floor plans, and 3D building models.
The novel contributions of this dissertation fall into three groups. First, we present multiple methods for producing 2D floor plans in a scalable fashion. We automatically partition these floor plans into separate building levels and separate rooms within each level. These floor plans can also be extruded into simplified 2.5D models of the building environment. Second, we present multiple techniques to produce complex 3D models of the scanned environment, capturing all observed detail. Third, we present several techniques that combine these two types of building models -- simple and complex -- to perform additional analysis of the building environment. Such analysis includes segmenting objects and furniture in the environment as separate models, reducing noise and artifacts within the models, and demonstrating novel visualization techniques. Additionally, we show several example datasets generated by the system in real-world environments, including buildings of over 40,000 square feet. We demonstrate how such building models are applicable in many fields of study, including architecture, building energy efficiency, virtual walk-throughs of buildings, indoor navigation, and augmented and virtual reality.},
}
EndNote citation:
%0 Thesis %A Turner, Eric %T 3D Modeling of Interior Building Environments and Objects from Noisy Sensor Suites %I EECS Department, University of California, Berkeley %D 2015 %8 May 14 %@ UCB/EECS-2015-105 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-105.html %F Turner:EECS-2015-105