A Deep Learning Network for Validation of 3D Point Cloud Surface Registration
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Authors
Mazumder, Joy
Date
Type
thesis
Language
eng
Keyword
Surface Registration , 3D Object Recognition , Validation , Shared mlp Network , Point Cloud
Alternative Title
Abstract
This thesis proposes a novel deep learning architecture called ValidNet to automatically validate the correctness of a 3D surface registration outcome. The performance of many tasks such as object detection mainly depends on the applied registration algorithms, which themselves are susceptible to local minima. Revealing this tendency and verifying the success of registration algorithms is a difficult task. We treat this as a classification problem, and propose a two-class classifier to distinguish clearly between true positive and false positive registration outcomes. Our proposed ValidNet system deploys a shared multilayer perceptron architecture which works on the raw and unordered point cloud data of scene and model points. This network is able to perform the two fundamental tasks of feature extraction and similarity matching using the powerful capability of a deep neural network. Experiments on a large synthetic dataset show that the proposed method can effectively be used in automatic validation of 3D surface registration.
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License
Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 3.0 United States
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 3.0 United States