Inferring the Existence of Geometric Primitives to Represent Non-Discriminable Data
Name
PeraireBueno-pjba-SM-AeroAstro-2021-thesis.pdf
Description
Thesis PDF
Size
15.31 MB
Format
Adobe PDF
Checksum (MD5)
1404e7bab711e8789f884f3c069a788d
Author(s)
Peraire-Bueno, James A.
Advisor(s)
Roy, Nicholas
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we set out to find an algorithm that uses only geometric primitives to represent an input pointcloud. In addition to the problems faced in general primitive fitting, non-discriminable data presents additional data association challenges. We propose to address these challenges by estimating the existence rather than parameters of geometric primitives, and explore various options to do so. We first explore a sampling-based Markov-Chain Monte-Carlo approach together with a ray likelihood model. We then explore a neural network approach and finish by presenting a method to make the Chamfer distance differentiable with respect to primitive existence.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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