3D Object Detection in Context
Document Type
Dissertation
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.
First Page
i
Last Page
43
Publication Date
12-1-2022
Recommended Citation
S.M.J. Abu Ghazal, "3D Object Detection in Context", M.S. Thesis, Computer Vision, MBZUAI, Abu Dhabi, UAE, 2022.
COinS
Comments
Thesis submitted to the Deanship of Graduate and Postdoctoral Studies
In partial fulfillment of the requirements for the M.Sc degree in Computer Vision
Advisors: Dr. Rao Anwer, Dr. Muhammad Haris
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