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Delft University of Technology
1.
Ai, Zhiwei (author).
Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d 
► Deep learning methods have been demonstrated to be promising in semantic segmentation of point clouds. Existing works focus on extracting informative local features based on…
(more)
▼ Deep learning methods have been demonstrated to be promising in semantic segmentation of point clouds. Existing works focus on extracting informative local features based on individual points and their local neighborhood. They lack consideration of the general structures and latent contextual relations of underlying shapes among points. To this end, we design geometric priors to encode contextual relations of underlying shapes between corresponding point pairs. Geometric prior convolution operator is proposed to explicitly incorporate the contextual relations into the computation. Then, GP-net, which contains geometric prior convolution and a backbone network is constructed. Our experiments show that the performance of our backbone network can be improved by up to 6.9 percent in terms of mean Intersection over Union (mIoU) with the help of geometric prior convolution. We also analyze different design options of geometric prior convolution and GP-net. The GP-net has been tested on the Paris and Lille 3D benchmark, and it achieves the state-of-the-art performance of 74.7 % mIoU.
Mechanical Engineering
Advisors/Committee Members: Nan, Liangliang (mentor), Gavrila, Dariu (graduation committee), Lindenbergh, Roderik (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Deep Learning; Point Clouds; Semantic Segmentation
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APA (6th Edition):
Ai, Z. (. (2019). Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d
Chicago Manual of Style (16th Edition):
Ai, Zhiwei (author). “Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d.
MLA Handbook (7th Edition):
Ai, Zhiwei (author). “Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.” 2019. Web. 01 Mar 2021.
Vancouver:
Ai Z(. Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d.
Council of Science Editors:
Ai Z(. Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d
Delft University of Technology
2.
Arapakis, Takis (author); van Heerden, Natasja (author); Rodriguez-Mon Barrera, Guillermo (author); Wang, Qu (author).
Digitizing Real-World Scenes from Images.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:103f2d94-cc84-4582-a25f-a0fc11461f18
► 3D computer models are starting to play a more and more important role in our society. Realworldsituations are often too complex to explain in a…
(more)
▼ 3D computer models are starting to play a more and more important role in our society. Realworldsituations are often too complex to explain in a 2D map and also the interest in virtualreality, serious gaming and other technologies that can be based on 3D computer models, isgrowing.
Synthesis Project 2018
Geomatics
Advisors/Committee Members: Nan, Liangliang (mentor), Peters, Ravi (mentor), Delft University of Technology (degree granting institution).
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APA (6th Edition):
Arapakis, Takis (author); van Heerden, Natasja (author); Rodriguez-Mon Barrera, Guillermo (author); Wang, Q. (. (2018). Digitizing Real-World Scenes from Images. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:103f2d94-cc84-4582-a25f-a0fc11461f18
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Arapakis, Takis (author); van Heerden, Natasja (author); Rodriguez-Mon Barrera, Guillermo (author); Wang, Qu (author). “Digitizing Real-World Scenes from Images.” 2018. Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:103f2d94-cc84-4582-a25f-a0fc11461f18.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Arapakis, Takis (author); van Heerden, Natasja (author); Rodriguez-Mon Barrera, Guillermo (author); Wang, Qu (author). “Digitizing Real-World Scenes from Images.” 2018. Web. 01 Mar 2021.
Vancouver:
Arapakis, Takis (author); van Heerden, Natasja (author); Rodriguez-Mon Barrera, Guillermo (author); Wang Q(. Digitizing Real-World Scenes from Images. [Internet] [Thesis]. Delft University of Technology; 2018. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:103f2d94-cc84-4582-a25f-a0fc11461f18.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Arapakis, Takis (author); van Heerden, Natasja (author); Rodriguez-Mon Barrera, Guillermo (author); Wang Q(. Digitizing Real-World Scenes from Images. [Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:103f2d94-cc84-4582-a25f-a0fc11461f18
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
3.
Bouzas, Vasileios (author).
Structure-aware Building Mesh Simplification.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:a0faf1a6-9815-4828-9186-a4a16119c71c
► In recent years, there is an ever-increasing demand — both by industry and academia — for 3D spatial information and especially, for 3D building models.…
(more)
▼ In recent years, there is an ever-increasing demand — both by industry and academia — for 3D spatial information and especially, for 3D building models. One of the ways to acquire such models derives from the combination of massive point clouds with reconstruction techniques, such as Ball Pivoting and Poisson Reconstruction. The result of these techniques is the representation of individual buildings or entire urban scenes in the form of surface meshes — data structures consisting of vertices, edges and faces. Despite their usefulness for visualization purposes, the high complexity of these meshes, along with various geometric and topological flaws, stands as an obstacle to their usage in further applications, such as simulations and urban planning. To address the issue of complexity, the production of lightweight building meshes can be achieved through mesh simplification, which reduces the amount of faces used in the original representations. Moreover, simplification methods focus on conforming the resulting mesh to the original one, in order to minimize the differences between the two of them. As a consequence, simple and accurate building models are possible to be acquired, whose geometric and topological validity is yet questionable. In this thesis, we introduce a novel approach for the simplification of building models, which results into a more compact representation, free of topological defects. The main characteristic of our method is structure awareness — namely, the recovery and preservation, for the input mesh, of both its primitives and the interrelationships between them (their configuration in 3D space). This awareness asserts that the resulting mesh closely follows the original and at the same time, dictates the geometric operations needed for its construction in the first place — thus providing accuracy, along with computational efficiency. Our proposed methodology is divided into three main stages: (a) primitive detection via mesh segmentation, (b) storage of primitive interrelationships in a structure graph and (c) simplification. In particular, simplification is accomplished here by approximating the primitive borders with a building scaffold, out of which a set of candidate faces is defined. The selection of faces from the candidate set to form the simplified mesh is achieved through the formulation of a linear binary programming problem, along with certain hard constraints to ensure that this mesh is both manifold and watertight. Experimentation reveals that our simplification method is able to produce simpler representations for both closed and open building meshes, which highly conform to the initial structure and are ready to be used for spatial analysis. Additionally, a fairly good approximation of a given mesh is possible to be obtained within reasonable execution times, regardless of the initial noise level or topological invalidity. Finally, a comparative analysis shows that the accuracy of our method stands in parallel with that of other available simplification techniques.
Geomatics
Advisors/Committee Members: Nan, Liangliang (mentor), Ledoux, Hugo (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: MVS meshes; Structure Awareness; Simplification; Topological Validity
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APA (6th Edition):
Bouzas, V. (. (2019). Structure-aware Building Mesh Simplification. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a0faf1a6-9815-4828-9186-a4a16119c71c
Chicago Manual of Style (16th Edition):
Bouzas, Vasileios (author). “Structure-aware Building Mesh Simplification.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:a0faf1a6-9815-4828-9186-a4a16119c71c.
MLA Handbook (7th Edition):
Bouzas, Vasileios (author). “Structure-aware Building Mesh Simplification.” 2019. Web. 01 Mar 2021.
Vancouver:
Bouzas V(. Structure-aware Building Mesh Simplification. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:a0faf1a6-9815-4828-9186-a4a16119c71c.
Council of Science Editors:
Bouzas V(. Structure-aware Building Mesh Simplification. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a0faf1a6-9815-4828-9186-a4a16119c71c
Delft University of Technology
4.
Du, Shenglan (author).
Accurate, Detailed and Automatic Tree Modelling from Point Clouds.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:675ee438-3dc9-43c8-a7a7-f3b8807c583f
► Trees are of great significance throughout the world, both in urban scenes and in natural environments. Models of trees can be widely applied in various…
(more)
▼ Trees are of great significance throughout the world, both in urban scenes and in natural environments. Models of trees can be widely applied in various fields, for instance, landscape design, geo-simulation, environment modelling, and forestry inventories. Recently, laser scanning technology has been rapidly developed, making it possible to effectively acquire geometric attributes of trees and achieve accurate 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user interactions. In this thesis, we propose a novel method to accurately and automatically reconstruct tree branches from laser scanned points. We first employ the Minimum Spanning Tree (MST) algorithm to extract an initial tree skeleton over the single tree point cloud, then simplify the skeleton through iterative removal of redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. The results show that our approach is adaptable to various trees with different data qualities. We also demonstrate both the topological fidelity and geometrical accuracy of our approach without significant user interactions. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.
Geomatics
Advisors/Committee Members: Nan, Liangliang (mentor), Lindenbergh, Roderik (mentor), Spaans, Marjolein (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: laser scanning; point cloud; individual tree modelling
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APA (6th Edition):
Du, S. (. (2019). Accurate, Detailed and Automatic Tree Modelling from Point Clouds. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:675ee438-3dc9-43c8-a7a7-f3b8807c583f
Chicago Manual of Style (16th Edition):
Du, Shenglan (author). “Accurate, Detailed and Automatic Tree Modelling from Point Clouds.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:675ee438-3dc9-43c8-a7a7-f3b8807c583f.
MLA Handbook (7th Edition):
Du, Shenglan (author). “Accurate, Detailed and Automatic Tree Modelling from Point Clouds.” 2019. Web. 01 Mar 2021.
Vancouver:
Du S(. Accurate, Detailed and Automatic Tree Modelling from Point Clouds. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:675ee438-3dc9-43c8-a7a7-f3b8807c583f.
Council of Science Editors:
Du S(. Accurate, Detailed and Automatic Tree Modelling from Point Clouds. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:675ee438-3dc9-43c8-a7a7-f3b8807c583f
Delft University of Technology
5.
Garg, Chirag (author).
Indoor 3D Reconstruction from a Single Image.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0
► 3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose…
(more)
▼ 3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose use sensor devices and multiple images for data acquisition and extracting 3D information and representation of the scene, with the advent of deep learning techniques, there has been good progress in extracting 3D information of an indoor scene reconstruction using a single image. This has potential in minimizing user efforts and cost for data acquisition. The current state-of-the-art method involves two main components, the global depth map and plane instances. After investigating the current state-of-the-art methods, it is observed that there is inconsistency in reconstructed surface boundaries and depth estimation over the curvature and edges of the objects present in the scene, despite having good 3D representation in the surrounding regions. We devise a loss function for optimizing depth estimation during supervision of the neural network by providing geometric awareness to the pixels at local level based on its neighborhood properties defined by spatial compatibility and color similarity. A similar function is used during 3D reconstruction for orientation consistency in the point cloud. Based on the quantitative and qualitative analysis, it is observed that the proposed approach helps in improving the 3D reconstruction from a single image in an indoor environment.
Geomatics
Advisors/Committee Members: Nan, Liangliang (mentor), van Gemert, Jan (mentor), Khademi, Seyran (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: 3D Reconstruction; Deep Learning; indoor reconstruction; Piecewise Planar Reconstruction; Point Cloud; 3D Model; Convolutional Neural Networks; Depth reconstruction; Supervised Learning; Indoor environment; Single Image
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APA (6th Edition):
Garg, C. (. (2020). Indoor 3D Reconstruction from a Single Image. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0
Chicago Manual of Style (16th Edition):
Garg, Chirag (author). “Indoor 3D Reconstruction from a Single Image.” 2020. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0.
MLA Handbook (7th Edition):
Garg, Chirag (author). “Indoor 3D Reconstruction from a Single Image.” 2020. Web. 01 Mar 2021.
Vancouver:
Garg C(. Indoor 3D Reconstruction from a Single Image. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0.
Council of Science Editors:
Garg C(. Indoor 3D Reconstruction from a Single Image. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0
Delft University of Technology
6.
Kaniouras, Pantelis (author).
Road Detection from Remote Sensing Imagery.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03
► Road network maps facilitate a great number of applications in our everyday life. However, their automatic creation is a difficult task, and so far, published…
(more)
▼ Road network maps facilitate a great number of applications in our everyday life. However, their automatic creation is a difficult task, and so far, published methodologies cannot provide reliable solutions. The common and most recent approach is to design a road detection algorithm from remote sensing imagery based on a Convolutional Neural Network, followed by a result refinement post-processing step. In this project I proposed a deep learning model that utilized the Multi-Task Learning technique to improve the performance of the road detection task by incorporating prior knowledge constraints. Multi-Task Learning is a mechanism whose objective is to improve a model's generalization performance by exploiting information retrieved from the training signals of related tasks as an inductive bias, and, as its name suggests, solve multiple tasks simultaneously. Carefully selecting which tasks will be jointly solved favors the preservation of specific properties of the target object, in this case, the road network. My proposed model is a Multi-Task Learning U-Net with a ResNet34 encoder, pre-trained on the ImageNet dataset, that solves for the tasks of Road Detection Learning, Road Orientation Learning, and Road Intersection Learning. Combining the capabilities of the U-Net model, the ResNet encoder and the constrained Multi-Task Learning mechanism, my model achieved better performance both in terms of image segmentation and topology preservation against the baseline single-task solving model. The project was based on the publicly available SpaceNet Roads Dataset.
Geomatics
Advisors/Committee Members: Nan, Liangliang (mentor), Lindenbergh, Roderik (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: multi-task learning; deep learning; road detection; Convolutional Neural Network
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APA (6th Edition):
Kaniouras, P. (. (2020). Road Detection from Remote Sensing Imagery. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03
Chicago Manual of Style (16th Edition):
Kaniouras, Pantelis (author). “Road Detection from Remote Sensing Imagery.” 2020. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03.
MLA Handbook (7th Edition):
Kaniouras, Pantelis (author). “Road Detection from Remote Sensing Imagery.” 2020. Web. 01 Mar 2021.
Vancouver:
Kaniouras P(. Road Detection from Remote Sensing Imagery. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03.
Council of Science Editors:
Kaniouras P(. Road Detection from Remote Sensing Imagery. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:21fc20a8-455d-4583-9698-4fea04516f03
Delft University of Technology
7.
Li, Jiahui (author).
Attention-Aware Age-Agnostic Visual Place Recognition.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7
► A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is…
(more)
▼ A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is commonly treated as an image retrieval task, where a query image from an unknown location is matched with relevant instances from geo-tagged gallery database. Different from conventional VPR settings where the query images and gallery images come from the same domain, we propose a more common but challenging setup where the query images are collected under a new unseen condition. The two domains involved in this work are contemporary street view images of Amsterdam from the Mapillary dataset (source domain) and historical images of the same city from Beeldbank dataset (target domain). We tailored an age-invariant feature learning CNN that can focus on domain invariant objects and learn to match images based on a weakly supervised ranking loss. We propose an attention aggregation module that is robust to domain discrepancy between the train and the test data. Further, a multi-kernel maximum mean discrepancy (MK-MMD) domain adaptation loss is adopted to improve the cross-domain ranking performance. Both attention and adaptation modules are unsupervised while the ranking loss uses weak supervision. Visual inspection shows that the attention module focuses on built forms while the dramatically changing environment are less weighed. Our proposed CNN achieves state of the art results (99% accuracy) on the single-domain VPR task and 20% accuracy at its best on the cross-domain VPR task, revealing the difficulty of age-invariant VPR.
Advisors/Committee Members: van Gemert, Jan (mentor), Khademi, Seyran (mentor), Wang, Ziqi (mentor), Reinders, Marcel (graduation committee), Nan, Liangliang (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Attention Mechanism
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APA ·
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APA (6th Edition):
Li, J. (. (2019). Attention-Aware Age-Agnostic Visual Place Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7
Chicago Manual of Style (16th Edition):
Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.
MLA Handbook (7th Edition):
Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Web. 01 Mar 2021.
Vancouver:
Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.
Council of Science Editors:
Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7
Delft University of Technology
8.
Li, Weiran (author).
Detection of subsurface meltwater in East Antarctica using SAR Interferometry.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:783417aa-7e14-4e35-a362-9440ab0b6bc8
► Climate change has been a heated topic in recent years, and the mass loss of ice sheets is one aspect of it. The Antarctic Ice…
(more)
▼ Climate change has been a heated topic in recent years, and the mass loss of ice sheets is one aspect of it. The Antarctic Ice Sheet has experienced certain mass loss in the form of ice-shelf collapse and (sub-)surface melting, but thorough study remains limited due to the remote location of the continent. Therefore, remote sensing is expected to provide valuable information on Antarctica, in order to monitor its mass balance and gain insights on the extent of climate change. As one of the factors to the mass loss in Antarctica, subsurface melt can be critical yet hard to capture. The limitation of remote sensing data, especially valid optical images over polar regions may add to the issue. This study aims to exploit the potentiality of SAR Interferometry in detecting subsurface melt, as the microwave bands are not affected by weather and illumination, and the interferometric data may provide supportive information reflecting the properties of the physical environment. It is expected that by using this technique, the gap can be filled in when optical images are not available, or pure SAR images are not informative. The technique is applied to two ice shelves in East Antarctica, Roi Baudouin Ice Shelf and Amery Ice Shelf. And this study is expected to be operated over a broader scale such as the Antarctic continent and Greenland.
Geomatics
Advisors/Committee Members: Indrajit, Agung (mentor), Lopez Dekker, Paco (mentor), Lhermitte, Stef (mentor), Nan, Liangliang (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Remote Sensing; InSAR; Antarctica
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, W. (. (2018). Detection of subsurface meltwater in East Antarctica using SAR Interferometry. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:783417aa-7e14-4e35-a362-9440ab0b6bc8
Chicago Manual of Style (16th Edition):
Li, Weiran (author). “Detection of subsurface meltwater in East Antarctica using SAR Interferometry.” 2018. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:783417aa-7e14-4e35-a362-9440ab0b6bc8.
MLA Handbook (7th Edition):
Li, Weiran (author). “Detection of subsurface meltwater in East Antarctica using SAR Interferometry.” 2018. Web. 01 Mar 2021.
Vancouver:
Li W(. Detection of subsurface meltwater in East Antarctica using SAR Interferometry. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:783417aa-7e14-4e35-a362-9440ab0b6bc8.
Council of Science Editors:
Li W(. Detection of subsurface meltwater in East Antarctica using SAR Interferometry. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:783417aa-7e14-4e35-a362-9440ab0b6bc8
Delft University of Technology
9.
Tzounakos, Nikos (author).
Robust Interior: Exterior Classification for 3D Models.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9a8ebd7a-b807-4b7f-a9f6-cce73cc5315b
► The use of 3D models has been rapidly expanding, finding applications in both scientific and commercial fields. One common requirement for these various applications is…
(more)
▼ The use of 3D models has been rapidly expanding, finding applications in both scientific and commercial fields. One common requirement for these various applications is the geometrical and topological validity of these models. However, many models available online contain deficiencies in various forms, such as duplicated geometry, gaps in the surface, etc.. To cope with those deficiencies, a standard solution is the clean extraction of the model’s boundary, and simultaneously the model’s reconstruction in a way that its structure is valid. This thesis tackles a more generalized problem, the inside-outside classification for these models. Where many approaches might have requirements for running analysis, the methodology presented strives to robustly handle all cases. These last decades, there have been various approaches in solving the ”inside - outside classification problem”. A major attempt utilizes the winding number algorithm, in order to assign values to elements whose position is relevant to the input model. By assessing that value, a decision on whether the element in question is interior or exterior is taken. Other approaches work with casting rays, or other geometric analysis to also identify the borders of a model and segment the interior from the exterior. Also, since deficiencies inhibit the kick-starting of the necessary analysis, there are methods that try to restructure said models in order to clear any existing deficiencies. The methodology within this thesis will attempt a different approach from those that have been presented until now, which is transferring the problem from three into two dimensions. The first step is introducing a planar cross section on the area of interest. From there, through some graph reconstruction, geometric and optimization applications, a valid 1-manifold boundary of the cross-section is created. On that, the application of inside-outside classification through ray casting is possible. Assessing the results of the pipeline proves that the automated process can produce valid results, for a particular point of interest, related to an input model. The pipeline has been proven to function regardless of the cutting plane’s orientation, and can handle robustly a multitude of geometrically and topologically defective models. The results from this thesis can inspire further applications, and improvements on the pipeline can further evolve the quality of its outcome.
Geomatics
Advisors/Committee Members: Nan, Liangliang (mentor), Ledoux, Hugo (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Inside; Outside; Classification; 3D; Model; 2D; Planes; Ray Casting
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APA (6th Edition):
Tzounakos, N. (. (2019). Robust Interior: Exterior Classification for 3D Models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9a8ebd7a-b807-4b7f-a9f6-cce73cc5315b
Chicago Manual of Style (16th Edition):
Tzounakos, Nikos (author). “Robust Interior: Exterior Classification for 3D Models.” 2019. Masters Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:9a8ebd7a-b807-4b7f-a9f6-cce73cc5315b.
MLA Handbook (7th Edition):
Tzounakos, Nikos (author). “Robust Interior: Exterior Classification for 3D Models.” 2019. Web. 01 Mar 2021.
Vancouver:
Tzounakos N(. Robust Interior: Exterior Classification for 3D Models. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:9a8ebd7a-b807-4b7f-a9f6-cce73cc5315b.
Council of Science Editors:
Tzounakos N(. Robust Interior: Exterior Classification for 3D Models. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9a8ebd7a-b807-4b7f-a9f6-cce73cc5315b
Delft University of Technology
10.
van Dongen, Kirsten (author).
Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815
► Trees are an important aspect of the world around us, and play a sufficient role in our daily lives. They contribute to human health and…
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▼ Trees are an important aspect of the world around us, and play a sufficient role in our daily lives. They contribute to human health and well-being in various ways. Tree inventory and monitoring are of great interest for biomass estimations and changes in the purifying effect on the air. It is a very time consuming and cost inefficient way to check every tree in and around a city or town, therefore there is further research required in the use of AHN data. Together with the “tree information data set” formthemunicipality ofDelft, the location and the corresponding point cloud of tree different species of trees are selected. For the species of interest, Aesculus Hippocastanum, Acer Saccharinum and Platanus x Hispanica, different characteristics are determined. In this research six different characteristics are estimated; Height, Trunk Height, Normalized Trunk Height, Canopy Projected Area, Normalized Canopy Projected Area, Ratio of Diameters, Normalized Ratio of Diameter, Centre of Gravity and at least the Normalized Centre of Gravity. These characteristics are used as features for the Random Forest Classification, Consequently the Confusion Matrix is used as performance measurement. The results of a test of 30 pointclouds, per species of interest, show that the Random Forest Classification is able to classify individual trees. However, these three different species cannot by sufficiently classified using clustering.
Advisors/Committee Members: Lindenbergh, Roderik (mentor), Nan, Liangliang (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: AHN; tree classification; laser point data
Record Details
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Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
van Dongen, K. (. (2019). Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
van Dongen, Kirsten (author). “Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis.” 2019. Thesis, Delft University of Technology. Accessed March 01, 2021.
http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
van Dongen, Kirsten (author). “Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis.” 2019. Web. 01 Mar 2021.
Vancouver:
van Dongen K(. Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis. [Internet] [Thesis]. Delft University of Technology; 2019. [cited 2021 Mar 01].
Available from: http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
van Dongen K(. Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis. [Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
. 
Open Access Theses and Dissertations