Heritage Reproduction in the Age of High-Resolution Scanning:A Critical Evaluation of Digital Infilling Methods for Historic Preservation

High-definition digital scanning has established itself as a useful tool for documenting cultural heritage in the twenty-first century. Proponents of surveying technology are hailing the use of digital fact-based 3D models as valuable tools for recording, analyzing and safeguarding items of cultural importance. Methods for digitally filling holes have not yet been considered through the lens of historic preservation. No modeling technique is error-free and understanding how heritage professionals are addressing lacunae is vital for understanding digital heritage objects resulting from 3D scanning hardware. Frameworks exist for working with scanned data, but they define general principles for a broad range of applications and do not provide any guidelines or strategies of how to comply with them practically. This thesis is a comparative evaluation of current practices of in-filling digital lacunae that attempts to establish which methods are best suited to the following historic preservation practices: documentation, Interpretation graphics, Long-term monitoring, digital restoration, physical fabrication

A comparison of hole-filling methods in 3D

This paper presents a review of the most relevant current techniques that deal with hole-filling in 3D models. Contrary to earlier reports, which approach mesh repairing in a sparse and global manner, the objective of this review is twofold. First, a specific and comprehensive review of hole-filling techniques (as a relevant part in the field of mesh repairing) is carried out. We present a brief summary of each technique with attention paid to its algorithmic essence, main contributions and limitations. Second, a solid comparison between 34 methods is established. To do this, we define 19 possible meaningful features and properties that can be found in a generic hole-filling process. Then, we use these features to assess the virtues and deficiencies of the method and to build comparative tables. The purpose of this review is to make a comparative hole-filling state-of-the-art available to researchers, showing pros and cons in a common framework.• Ministerio de Economía y Competitividad: Proyecto DPI2013-43344-R (I+D+i) • Gobierno de Castilla-La Mancha: Proyecto PEII-2014-017-PpeerReviewe

Fitting and filling of 3D datasets with volume constraints using radial basis functions under tension

Acknowledgments This work was supported by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Research Project A-FQM-76-UGR20, University of Granada) and by the Junta de Andalucía (Research Groups FQM-191 and TEP-190). Funding for open access charge: Universidad de Granada / CBUA.Given a dataset of 3D points in which there is a hole, i.e., a region with a lack of information, we develop a method providing a surface that fits the dataset and fills the hole. The filling patch is required to fulfill a prescribed volume condition. The fitting–filling function consists of a radial basis functions that minimizes an energy functional involving both, the fitting of the dataset and the volume constraint of the filling patch, as well as the fairness of the function. We give a convergence result and we present some graphical and numerical examples.FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Research Project A-FQM-76-UGR20, University of Granada)Junta de Andalucía (Research Groups FQM-191 and TEP-190)Funding for open access charge: Universidad de Granada / CBU

Automatic Linear and Curvilinear Mesh Generation Driven by Validity Fidelity and Topological Guarantees

Image-based geometric modeling and mesh generation play a critical role in computational biology and medicine. In this dissertation, a comprehensive computational framework for both guaranteed quality linear and high-order automatic mesh generation is presented. Starting from segmented images, a quality 2D/3D linear mesh is constructed. The boundary of the constructed mesh is proved to be homeomorphic to the object surface. In addition, a guaranteed dihedral angle bound of up to 19:47o for the output tetrahedra is provided. Moreover, user-specified guaranteed bounds on the distance between the boundaries of the mesh and the boundaries of the materials are allowed. The mesh contains a small number of mesh elements that comply with these guarantees, and the runtime is compatible in performance with other software. Then the curvilinear mesh generator allows for a transformation of straight-sided meshes to curvilinear meshes with C1 or C2 smooth boundaries while keeping all elements valid and with good quality as measured by their Jacobians. The mathematical proof shows that the meshes generated by our algorithm are guaranteed to be homeomorphic to the input images, and all the elements inside the meshes are guaranteed to be with good quality. Experimental results show that the mesh boundaries represent the objects\u27 shapes faithfully, and the accuracy of the representation is improved compared to the corresponding linear mesh

Effective field theories for strongly correlated fermions - Insights from the functional renormalization group

'There are very few things that can be proved rigorously in condensed matter physics.' These famous words, brought to us by Nobel laureate Anthony James Leggett in 2003, summarize very well the challenging nature of problems researchers find themselves confronted with when entering the fascinating field of condensed matter physics. The former roots in the inherent many-body character of several quantum mechanical particles with modest to strong interactions between them: their individual properties might be easy to understand, while their collective behavior can be utterly complex. Strongly correlated electron systems, for example, exhibit several captivating phenomena such as superconductivity or spin-charge separation at temperatures far below the energy scale set by their mutual couplings. Moreover, the dimension of the respective Hilbert space grows exponentially, which impedes the exact diagonalization of their Hamiltonians in the thermodynamic limit. For this reason, renormalization group (RG) methods have become one of the most powerful tools of condensed matter research - scales are separated and dealt with iteratively by advancing an RG flow from the microscopic theory into the low-energy regime. In this thesis, we report on two complementary implementations of the functional renormalization group (fRG) for strongly correlated electrons. Functional RG is based on an exact hierarchy of coupled differential equations, which describe the evolution of one-particle irreducible vertices in terms of an infrared cutoff Lambda. To become amenable to numerical solutions, however, this hierarchy needs to be truncated. For sufficiently weak interactions, three-particle and higher-order vertices are irrelevant at the infrared fixed point, justifying their neglect. This one-loop approximation lays the foundation for the N-patch fRG scheme employed within the scope of this work. As an example, we study competing orders of spinless fermions on the triangular lattice, mapping out a rich phase diagram with several charge and pairing instabilities. In the strong-coupling limit, a cutting-edge implementation of the multiloop pseudofermion functional renormalization group (pffRG) for quantum spin systems at zero temperature is presented. Despite the lack of a kinetic term in the microscopic theory, we provide evidence for self-consistency of the method by demonstrating loop convergence of pseudofermion vertices, as well as robustness of susceptibility flows with respect to occupation number fluctuations around half-filling. Finally, an extension of pffRG to Hamiltonians with coupled spin and orbital degrees of freedom is discussed and results for exemplary model studies on strongly correlated electron systems are presented

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing

Small Unmanned Aircraft Systems (sUAS) operating in low-altitude airspace require flight near buildings and over people. Robust urgent landing capabilities including landing site selection are needed. However, conventional fixed-wing emergency landing sites such as open fields and empty roadways are rare in cities. This motivates our work to uniquely consider unoccupied flat rooftops as possible nearby landing sites. We propose novel methods to identify flat rooftop buildings, isolate their flat surfaces, and find touchdown points that maximize distance to obstacles. We model flat rooftop surfaces as polygons that capture their boundaries and possible obstructions on them. This thesis offers five specific contributions to support urgent rooftop landing. First, the Polylidar algorithm is developed which enables efficient non-convex polygon extraction with interior holes from 2D point sets. A key insight of this work is a novel boundary following method that contrasts computationally expensive geometric unions of triangles. Results from real-world and synthetic benchmarks show comparable accuracy and more than four times speedup compared to other state-of-the-art methods. Second, we extend polygon extraction from 2D to 3D data where polygons represent flat surfaces and interior holes representing obstacles. Our Polylidar3D algorithm transforms point clouds into a triangular mesh where dominant plane normals are identified and used to parallelize and regularize planar segmentation and polygon extraction. The result is a versatile and extremely fast algorithm for non-convex polygon extraction of 3D data. Third, we propose a framework for classifying roof shape (e.g., flat) within a city. We process satellite images, airborne LiDAR point clouds, and building outlines to generate both a satellite and depth image of each building. Convolutional neural networks are trained for each modality to extract high level features and sent to a random forest classifier for roof shape prediction. This research contributes the largest multi-city annotated dataset with over 4,500 rooftops used to train and test models. Our results show flat-like rooftops are identified with > 90% precision and recall. Fourth, we integrate Polylidar3D and our roof shape prediction model to extract flat rooftop surfaces from archived data sources. We uniquely identify optimal touchdown points for all landing sites. We model risk as an innovative combination of landing site and path risk metrics and conduct a multi-objective Pareto front analysis for sUAS urgent landing in cities. Our proposed emergency planning framework guarantees a risk-optimal landing site and flight plan is selected. Fifth, we verify a chosen rooftop landing site on real-time vertical approach with on-board LiDAR and camera sensors. Our method contributes an innovative fusion of semantic segmentation using neural networks with computational geometry that is robust to individual sensor and method failure. We construct a high-fidelity simulated city in the Unreal game engine with a statistically-accurate representation of rooftop obstacles. We show our method leads to greater than 4% improvement in accuracy for landing site identification compared to using LiDAR only. This work has broad impact for the safety of sUAS in cities as well as Urban Air Mobility (UAM). Our methods identify thousands of additional rooftop landing sites in cities which can provide safe landing zones in the event of emergencies. However, the maps we create are limited by the availability, accuracy, and resolution of archived data. Methods for quantifying data uncertainty or performing real-time map updates from a fleet of sUAS are left for future work.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170026/1/jdcasta_1.pd

Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic

A Computational Fluid-Structure Interaction Method for Simulating Supersonic Parachute Inflation

Following the successful landing of the Curiosity rover on the Martian surface in 2012, NASA/JPL conducted the low-density supersonic decelerator (LDSD) missions to develop large diameter parachutes to land the increasingly heavier payloads being sent to the Martian surface. Unexpectedly, both of the tested parachutes failed far below their design loads. It became clear that there was an inability to model and predict loads that occur during supersonic parachute inflation. In this dissertation, a new computational method that was developed to provide NASA with the capability to simulate supersonic parachute inflation is presented and validated. The method considers the loose coupling of two different immersed boundary methods with a nonlinear finite element solver. Following validation on canonical FSI problems, methods to simulate the permeability of parachute broadcloth and to identify and enforce contact in parallel are presented and validated. The coupled solvers are first applied to the supersonic parachute problem on a sub-scale MSL parachute and capsule geometry, and subsequently, a full-scale test flight from the Advanced Supersonic Parachute Inflation Research Experiments (ASPIRE) is simulated. To the best of the author’s knowledge, these are the first FSI simulations to match the ASPIRE flight test data