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Analysing the quality of PlaneRCNN plane parameters using a structure from motion system

Österberg, Fredrik LU (2021) In Master's Theses in Mathematical Sciences FMAM05 20202
Mathematics (Faculty of Engineering)
Abstract
This report aims to compare the plane parameters given by the PlaneRCNN network with those created by using a Structure from Motion (SfM) solution on the same data. Several datasets are used, and the results from PlaneRCNN are evaluated by using the camera matrices from the SfM solution and applying these along with scaling and coordinate transformations. We use and explain RANSAC, neural networks of different types, and general computer vision background material (such as methods of plane creation) required to understand the component parts of the report. A general analysis of the PlaneRCNN network is offered, using different performance metrics on varying sets of data, along with practical advice on how to best use it. The end result is... (More)
This report aims to compare the plane parameters given by the PlaneRCNN network with those created by using a Structure from Motion (SfM) solution on the same data. Several datasets are used, and the results from PlaneRCNN are evaluated by using the camera matrices from the SfM solution and applying these along with scaling and coordinate transformations. We use and explain RANSAC, neural networks of different types, and general computer vision background material (such as methods of plane creation) required to understand the component parts of the report. A general analysis of the PlaneRCNN network is offered, using different performance metrics on varying sets of data, along with practical advice on how to best use it. The end result is that we find a good method for comparing the PlaneRCNN plane parameters to those generated by SfM systems. We also note under what conditions we found PlaneRCNN to give the best results. (Less)
Please use this url to cite or link to this publication:
author
Österberg, Fredrik LU
supervisor
organization
course
FMAM05 20202
year
type
H2 - Master's Degree (Two Years)
subject
keywords
structure from motion, PlaneRCNN, neural network, sfm, computer vision, RANSAC, planes, room reconstruction
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3442-2021
ISSN
1404-6342
other publication id
2021:E19
language
English
id
9050565
date added to LUP
2021-08-20 15:48:50
date last changed
2021-08-20 15:48:50
@misc{9050565,
  abstract     = {{This report aims to compare the plane parameters given by the PlaneRCNN network with those created by using a Structure from Motion (SfM) solution on the same data. Several datasets are used, and the results from PlaneRCNN are evaluated by using the camera matrices from the SfM solution and applying these along with scaling and coordinate transformations. We use and explain RANSAC, neural networks of different types, and general computer vision background material (such as methods of plane creation) required to understand the component parts of the report. A general analysis of the PlaneRCNN network is offered, using different performance metrics on varying sets of data, along with practical advice on how to best use it. The end result is that we find a good method for comparing the PlaneRCNN plane parameters to those generated by SfM systems. We also note under what conditions we found PlaneRCNN to give the best results.}},
  author       = {{Österberg, Fredrik}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Analysing the quality of PlaneRCNN plane parameters using a structure from motion system}},
  year         = {{2021}},
}