Bayesian scene understanding with object-based latent representation and multi-modal sensor fusion
Name
1251801742-MIT.pdf
Size
2.31 MB
Format
Adobe PDF
Checksum (MD5)
bc5a424b601d635c1ac5c506c4ca78a8
Author(s)
Wallace, Michael A.,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
John W. Fisher III.
Date Issued
2021
Publisher
Massachusetts Institute of Technology
Abstract
Scene understanding systems transform observations of an environment into a representation that facilitates reasoning over that environment. In this context, many reasoning tasks benefit from a high-level, object-based scene representation; quantification of uncertainty; and multi-modal sensor fusion. Here, we present a method for scene understanding that achieves all three of these desiderata. First, we introduce a generative probabilistic model that couples an object-based latent scene representation with multiple observations of different modalities. We then provide an inference procedure that draws samples from the posterior distribution over scene representations given observations and their associated camera parameters. Finally, we demonstrate that this method recovers accurate, object-based representations of scenes, and provides uncertainty quantification at a high level of abstraction.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 73-75).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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