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Massachusetts Institute of Technology
Inferring the Existence of Geometric Primitives to Represent Non-Discriminable Data
Abstract
dc:description.abstractIn 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.
Degree
thesis:*- Name thesis:degree_name
- Master
- Department dc:contributor.department
- Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2021
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Peraire-Bueno, James A.
- Advisor dc:contributor.advisor
-
- Roy, Nicholas
Rights
dc:rights- Statement dc:rights
-
- In Copyright - Educational Use Permitted
- Copyright MIT
- Licence dc:rights.uri
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- https://hdl.handle.net/1721.1/139159
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/139159