3D Object Detection in Context
Abu Ghazal, Sultan Mobeen Jawdat
Abu Ghazal, Sultan Mobeen Jawdat
Supervisor
Department
Computer Vision
Embargo End Date
Type
Thesis
Date
2022
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Language
English
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Abstract
This work presents a second contribution attempting to push the performance of the contemporary state-of-the-art 3D object detector, RBGNet, by introducing self-attention on multiple levels. Inspired by self-attention is introduced at; (1) the point patch level to capture correlations between parts, or geometries, of objects, (2) the object candidate level to capture relationships between objects in the scene, and (3) the scene level to capture contextual cues. Through a series of experiments, the introduced self-attention modules prove to have a positive effect on the performance of the RBGNet baseline.
Citation
S.M.J. Abu Ghazal, "3D Object Detection in Context", M.S. Thesis, Computer Vision, MBZUAI, Abu Dhabi, UAE, 2022.