What Happened in 3D
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Date
2022
Publication Type
Master Thesis
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yes
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Abstract
Interpreting three-dimensional scenes that change over time is an open topic at its early stages. Current research mainly focuses on marking the existence of a difference between two temporal instances of the same scene without specifying the semantics of such difference. However, providing information on what has happened in an environment that changed over time can be helpful to a user only if the generated data is informative on the changes. In this project, I introduce Indoor Scene Activity Recognition, a new deep learning challenge that aligns elements of Action Classification and Change Detection. I annotate and collect a new dataset for this task by analyzing and manipulating another publicly available dataset, and I develop K3D, a dedicated two-stream three-dimensional neural network to tackle the challenge. Additionally, I create a two-dimensional convolutional baseline and an object multi-view baseline to benchmark K3D and compare the results in different settings.
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Examiner : Armeni, Iro
Examiner : Song, Shuran
Examiner : Pollefeys, Marc
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ETH Zurich
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03766 - Pollefeys, Marc / Pollefeys, Marc