Aerial reconstructions via probabilistic data fusion

In this thesis we propose a probabilistic model that incorporates multi-modal noisy measurements: aerial images and Light Detection and Ranging (LiDAR) to recover scene geometry and appearance in order to build a 3D photo-realistic model of a given scene. In urban environments, these reconstructions have many applications, such as surveillance, and urban planning. The proposed probabilistic model can be viewed as a data fusion model, in which the two data sources complement each other and allow for better results than when only a single one is present. Moreover, this modeling approach has the advantages that it can capture uncertainty in reconstructions, and the ability to incorporate additional scene measurements easily when the sensor models are available. Furthermore, the results obtained with the proposed method are qualitatively comparable to those obtained with traditional structure from motion, despite differences in modeling approach and reconstruction goals. The appearance and geometry trade-off present in the model between the different data sources can be used to obtain a similar (and sometime superior) reconstruction of complex urban scenes with fewer image observations over traditional reconstruction methods. Extending beyond reconstructions, the proposed model has two alluring features: first we are able to determine absolute scale and orientation, and secondly, we are able to detect moving objects. From an implementation standpoint, this thesis has shown how to leverage the power of graphic processing units (GPUs) and parallel programming to allow fast inference. Achieving real time rendering of scenes with hundreds of thousands of geometric primitives and inferring latent appearance, camera pose and geometry in the order of seconds each.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Cabezas, Randi

Advisor dc:contributor.advisor

• John W. Fisher, III.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/84904

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/84904