Extracting datums to reconstruct CSG models from 2D engineering sketches of polyhedral shapes

Our goal is to automatically generate CAD 3D models from 2D sketches as part of a design chain where models should be procedural, containing features arranged in a model tree and linked to suitable datums. Current procedural models capture much about the design intent and are easy to edit, but must be created from scratch during the detailed design state—given conceptual sketches as used by designers in the early part of the design process, current sketch-based modeling approaches only output explicit models. Thus, we describe an approach to extract high-level information directly from 2D engineering wireframe sketches and use it to complete a CSG feature tree, which serves as a model tree for a procedural 3D CAD model. Our method extracts procedural model information directly from 2D sketches in the form of a set of features, plus a set of datums and relationships between these features. We detect and analyze features of 2D sketches in isolation, and define the CSG feature tree by the parent–child relationships between features, and combine this information to obtain a complete and consistent CSG feature tree that can be transferred to a 3D modeler, which reconstructs the model. This paper focuses on how to extract the feature datums and the extrusion operation from an input 2D sketch.Funding for open access charge: CRUE-Universitat Jaume

Features and design intent in engineering sketches

We investigate the problem of determining design intent from engineering sketches: what did the designer have in mind when sketching a component? Specifically, we consider the unidirectional reverse mapping from form features, as determined from an input sketch, to design features, representing the design intent present in the designer’s mind. We introduce a list of com- mon engineering form features. For each, we list which geometrical cues may be helpful in identifying these features in design sketches, and we list the design features which such form features commonly imply. We show that a reductionist approach which decomposes a diagram into form features can be used to deduce the design intent of the object portrayed in a drawing. We supply experimental results in support of this idea

Parameter optimization and learning for 3D object reconstruction from line drawings.

Du, Hao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 61).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- 3D Reconstruction from 2D Line Drawings and its Applications --- p.1Chapter 1.2 --- Algorithmic Development of 3D Reconstruction from 2D Line Drawings --- p.3Chapter 1.2.1 --- Line Labeling and Realization Problem --- p.4Chapter 1.2.2 --- 3D Reconstruction from Multiple Line Drawings --- p.5Chapter 1.2.3 --- 3D Reconstruction from a Single Line Drawing --- p.6Chapter 1.3 --- Research Problems and Our Contributions --- p.12Chapter 2 --- Adaptive Parameter Setting --- p.15Chapter 2.1 --- Regularities in Optimization-Based 3D Reconstruction --- p.15Chapter 2.1.1 --- Face Planarity --- p.18Chapter 2.1.2 --- Line Parallelism --- p.19Chapter 2.1.3 --- Line Verticality --- p.19Chapter 2.1.4 --- Isometry --- p.19Chapter 2.1.5 --- Corner Orthogonality --- p.20Chapter 2.1.6 --- Skewed Facial Orthogonality --- p.21Chapter 2.1.7 --- Skewed Facial Symmetry --- p.22Chapter 2.1.8 --- Line Orthogonality --- p.24Chapter 2.1.9 --- Minimum Standard Deviation of Angles --- p.24Chapter 2.1.10 --- Face Perpendicularity --- p.24Chapter 2.1.11 --- Line Collinearity --- p.25Chapter 2.1.12 --- Whole Symmetry --- p.25Chapter 2.2 --- Adaptive Parameter Setting in the Objective Function --- p.26Chapter 2.2.1 --- Hill-Climbing Optimization Technique --- p.28Chapter 2.2.2 --- Adaptive Weight Setting and its Explanations --- p.29Chapter 3 --- Parameter Learning --- p.33Chapter 3.1 --- Construction of A Large 3D Object Database --- p.33Chapter 3.2 --- Training Dataset Generation --- p.34Chapter 3.3 --- Parameter Learning Framework --- p.37Chapter 3.3.1 --- Evolutionary Algorithms --- p.38Chapter 3.3.2 --- Reconstruction Error Calculation --- p.39Chapter 3.3.3 --- Parameter Learning Algorithm --- p.41Chapter 4 --- Experimental Results --- p.45Chapter 4.1 --- Adaptive Parameter Setting --- p.45Chapter 4.1.1 --- Use Manually-Set Weights --- p.45Chapter 4.1.2 --- Learn the Best Weights with Different Strategies --- p.48Chapter 4.2 --- Evolutionary-Algorithm-Based Parameter Learning --- p.49Chapter 5 --- Conclusions and Future Work --- p.53Bibliography --- p.5

Automatic creation of boundary-representation models from single line drawings

This thesis presents methods for the automatic creation of boundary-representation models of polyhedral objects from single line drawings depicting the objects. This topic is important in that automated interpretation of freehand sketches would remove a bottleneck in current engineering design methods. The thesis does not consider conversion of freehand sketches to line drawings or methods which require manual intervention or multiple drawings. The thesis contains a number of novel contributions to the art of machine interpretation of line drawings. Line labelling has been extended by cataloguing the possible tetrahedral junctions and by development of heuristics aimed at selecting a preferred labelling from many possible. The ”bundling” method of grouping probably-parallel lines, and the use of feature detection to detect and classify hole loops, are both believed to be original. The junction-line-pair formalisation which translates the problem of depth estimation into a system of linear equations is new. Treating topological reconstruction as a tree-search is not only a new approach but tackles a problem which has not been fully investigated in previous work

'Sawfish' Photonic Crystal Cavity for Near-Unity Emitter-to-Fiber Interfacing in Quantum Network Applications

Photon loss is one of the key challenges to overcome in complex photonic quantum applications. Photon collection efficiencies directly impact the amount of resources required for measurement-based quantum computation and communication networks. Promising resources include solid-state quantum light sources, however, efficiently coupling light from a single quantum emitter to a guided mode remains demanding. In this work, we eliminate photon losses by maximizing coupling efficiencies in an emitter-to-fiber interface. We develop a waveguide-integrated 'Sawfish' photonic crystal cavity and use finite element simulations to demonstrate that our system transfers, with 97.4% efficiency, the zero-phonon line emission of a negatively-charged tin vacancy center in diamond adiabatically to a single-mode fiber. A surrogate model trained by machine learning provides quantitative estimates of sensitivities to fabrication tolerances. Our corrugation-based design proves robust under state-of-the-art nanofabrication parameters, maintaining an emitter-to-fiber coupling efficiency of 88.6%. To demonstrate its potential in reducing resource requirements, we apply the Sawfish cavity to a recent one-way quantum repeater protocol.Comment: Main part: 16 pages, 7 figure

Cascades and transitions in turbulent flows

Turbulence is characterized by the non-linear cascades of energy and other inviscid invariants across a huge range of scales, from where they are injected to where they are dissipated. Recently, new experimental, numerical and theoretical works have revealed that many turbulent configurations deviate from the ideal 3D/2D isotropic cases characterized by the presence of a strictly direct/inverse energy cascade, respectively. We review recent works from a unified point of view and we present a classification of all known transfer mechanisms. Beside the classical cases of direct and inverse cascades, the different scenarios include: split cascades to small and large scales simultaneously, multiple/dual cascades of different quantities, bi-directional cascades where direct and inverse transfers of the same invariant coexist in the same scale-range and finally equilibrium states where no cascades are present, including the case when a condensate is formed. We classify all transitions as the control parameters are changed and we analyse when and why different configurations are observed. Our discussion is based on a set of paradigmatic applications: helical turbulence, rotating and/or stratified flows, MHD and passive/active scalars where the transfer properties are altered as one changes the embedding dimensions, the thickness of the domain or other relevant control parameters, as the Reynolds, Rossby, Froude, Peclet, or Alfven numbers. We discuss the presence of anomalous scaling laws in connection with the intermittent nature of the energy dissipation in configuration space. An overview is also provided concerning cascades in other applications such as bounded flows, quantum, relativistic and compressible turbulence, and active matter, together with implications for turbulent modelling. Finally, we present a series of open problems and challenges that future work needs to address.Comment: accepted for publication on Physics Reports 201

Recovering 3D geometry from single line drawings.

Xue, Tianfan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 52-55).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Previous Approaches on Face Identification --- p.3Chapter 1.1.1 --- Face Identification --- p.3Chapter 1.1.2 --- General Objects --- p.4Chapter 1.1.3 --- Manifold Objects --- p.7Chapter 1.2 --- Previous Approaches on 3D Reconstruction --- p.9Chapter 1.3 --- Our approach for Face Identification --- p.11Chapter 1.4 --- Our approach for 3D Reconstruction --- p.13Chapter 2 --- Face Detection --- p.14Chapter 2.1 --- GAFI and its Face Identification Results --- p.15Chapter 2.2 --- Our Face Identification Approach --- p.17Chapter 2.2.1 --- Real Face Detection --- p.18Chapter 2.2.2 --- The Weak Face Adjacency Theorem --- p.20Chapter 2.2.3 --- Searching for Type 1 Lost Faces --- p.22Chapter 2.2.4 --- Searching for Type 2 Lost Faces --- p.23Chapter 2.3 --- Experimental Results --- p.25Chapter 3 3 --- D Reconstruction --- p.30Chapter 3.1 --- Assumption and Terminology --- p.30Chapter 3.2 --- Finding Cuts from a Line Drawing --- p.34Chapter 3.2.1 --- Propositions for Finding Cuts --- p.34Chapter 3.2.2 --- Searching for Good Cuts --- p.35Chapter 3.3 --- Separation of a Line Drawing from Cuts --- p.38Chapter 3.4 3 --- D Reconstruction from a Line Drawing --- p.45Chapter 3.5 --- Experiments --- p.45Chapter 4 --- Conclusion --- p.5