3D reconstruction of curved objects from single 2D line drawings.

Wang, Yingze.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 42-47).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Related Work --- p.5Chapter 2.1 --- Line labeling and realization problem --- p.5Chapter 2.2 --- 3D reconstruction from multiple views --- p.6Chapter 2.3 --- 3D reconstruction from single line drawings --- p.7Chapter 2.3.1 --- Face identification from the line drawings --- p.7Chapter 2.3.2 --- 3D geometry reconstruction --- p.9Chapter 2.4 --- Our research topic and contributions --- p.13Chapter 3 --- Reconstruction of Curved Manifold Objects --- p.14Chapter 3.1 --- Assumptions and terminology --- p.14Chapter 3.2 --- Reconstruction of curved manifold objects --- p.17Chapter 3.2.1 --- Distinguishing between curved and planar faces --- p.17Chapter 3.2.2 --- Transformation of Line Drawings --- p.20Chapter 3.2.3 --- Regularities --- p.23Chapter 3.2.4 --- 3D Wireframe Reconstruction --- p.26Chapter 3.2.5 --- Generating Curved Faces --- p.28Chapter 3.2.6 --- The Complete 3D Reconstruction Algorithm --- p.33Chapter 4 --- Experiments --- p.35Chapter 5 --- Conclusions and Future Work --- p.40Chapter 5.1 --- Conclusions --- p.40Chapter 5.2 --- Future work --- p.40Bibliography --- p.4

3D object reconstruction from 2D and 3D line drawings.

Chen, Yu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 78-85).Abstracts in English and Chinese.Chapter 1 --- Introduction and Related Work --- p.1Chapter 1.1 --- Reconstruction from 2D Line Drawings and the Applications --- p.2Chapter 1.2 --- Previous Work on 3D Reconstruction from Single 2D Line Drawings --- p.4Chapter 1.3 --- Other Related Work on Interpretation of 2D Line Drawings --- p.5Chapter 1.3.1 --- Line Labeling and Superstrictness Problem --- p.6Chapter 1.3.2 --- CAD Reconstruction --- p.6Chapter 1.3.3 --- Modeling from Images --- p.6Chapter 1.3.4 --- Identifying Faces in the Line Drawings --- p.7Chapter 1.4 --- 3D Modeling Systems --- p.8Chapter 1.5 --- Research Problems and Our Contributions --- p.10Chapter 1.5.1 --- Recovering Complex Manifold Objects from Line Drawings --- p.10Chapter 1.5.2 --- The Vision-based Sketching System --- p.11Chapter 2 --- Reconstruction from Complex Line Drawings --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Assumptions and Terminology --- p.15Chapter 2.3 --- Separation of a Line Drawing --- p.17Chapter 2.3.1 --- Classification of Internal Faces --- p.18Chapter 2.3.2 --- Separating a Line Drawing along Internal Faces of Type 1 --- p.19Chapter 2.3.3 --- Detecting Internal Faces of Type 2 --- p.20Chapter 2.3.4 --- Separating a Line Drawing along Internal Faces of Type 2 --- p.28Chapter 2.4 --- 3D Reconstruction --- p.44Chapter 2.4.1 --- 3D Reconstruction from a Line Drawing --- p.44Chapter 2.4.2 --- Merging 3D Manifolds --- p.45Chapter 2.4.3 --- The Complete 3D Reconstruction Algorithm --- p.47Chapter 2.5 --- Experimental Results --- p.47Chapter 2.6 --- Summary --- p.52Chapter 3 --- A Vision-Based Sketching System for 3D Object Design --- p.54Chapter 3.1 --- Introduction --- p.54Chapter 3.2 --- The Sketching System --- p.55Chapter 3.3 --- 3D Geometry of the System --- p.56Chapter 3.3.1 --- Locating the Wand --- p.57Chapter 3.3.2 --- Calibration --- p.59Chapter 3.3.3 --- Working Space --- p.60Chapter 3.4 --- Wireframe Input and Object Editing --- p.62Chapter 3.5 --- Surface Generation --- p.63Chapter 3.5.1 --- Face Identification --- p.64Chapter 3.5.2 --- Planar Surface Generation --- p.65Chapter 3.5.3 --- Smooth Curved Surface Generation --- p.67Chapter 3.6 --- Experiments --- p.70Chapter 3.7 --- Summary --- p.72Chapter 4 --- Conclusion and Future Work --- p.74Chapter 4.1 --- Conclusion --- p.74Chapter 4.2 --- Future Work --- p.75Chapter 4.2.1 --- Learning-Based Line Drawing Reconstruction --- p.75Chapter 4.2.2 --- New Query Interface for 3D Object Retrieval --- p.75Chapter 4.2.3 --- Curved Object Reconstruction --- p.76Chapter 4.2.4 --- Improving the 3D Sketch System --- p.77Chapter 4.2.5 --- Other Directions --- p.77Bibliography --- p.7

3D object reconstruction from line drawings.

Cao Liangliang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 64-69).Abstracts in English and Chinese.Chapter 1 --- Introduction and Related Work --- p.1Chapter 1.1 --- Reconstruction from Single Line Drawings and the Applications --- p.1Chapter 1.2 --- Optimization-based Reconstruction --- p.2Chapter 1.3 --- Other Reconstruction Methods --- p.2Chapter 1.3.1 --- Line Labeling and Algebraic Methods --- p.2Chapter 1.3.2 --- CAD Reconstruction --- p.3Chapter 1.3.3 --- Modelling from Images --- p.3Chapter 1.4 --- Finding Faces of Line Drawings --- p.4Chapter 1.5 --- Generalized Cylinder --- p.4Chapter 1.6 --- Research Problems and Our Contribution --- p.5Chapter 1.6.1 --- A New Criteria --- p.5Chapter 1.6.2 --- Recover Objects from Line Drawings without Hidden Lines --- p.6Chapter 1.6.3 --- Reconstruction of Curved Objects --- p.6Chapter 1.6.4 --- Planar Limbs Assumption and the Derived Models --- p.6Chapter 2 --- A New Criteria for Reconstruction --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- Human Visual Perception and the Symmetry Measure --- p.10Chapter 2.3 --- Reconstruction Based on Symmetry and Planarity --- p.11Chapter 2.3.1 --- Finding Faces --- p.11Chapter 2.3.2 --- Constraint of Planarity --- p.11Chapter 2.3.3 --- Objective Function --- p.12Chapter 2.3.4 --- Reconstruction Algorithm --- p.13Chapter 2.4 --- Experimental Results --- p.13Chapter 2.5 --- Summary --- p.18Chapter 3 --- Line Drawings without Hidden Lines: Inference and Reconstruction --- p.19Chapter 3.1 --- Introduction --- p.19Chapter 3.2 --- Terminology --- p.20Chapter 3.3 --- Theoretical Inference of the Hidden Topological Structure --- p.21Chapter 3.3.1 --- Assumptions --- p.21Chapter 3.3.2 --- Finding the Degrees and Ranks --- p.22Chapter 3.3.3 --- Constraints for the Inference --- p.23Chapter 3.4 --- An Algorithm to Recover the Hidden Topological Structure --- p.25Chapter 3.4.1 --- Outline of the Algorithm --- p.26Chapter 3.4.2 --- Constructing the Initial Hidden Structure --- p.26Chapter 3.4.3 --- Reducing Initial Hidden Structure --- p.27Chapter 3.4.4 --- Selecting the Most Plausible Structure --- p.28Chapter 3.5 --- Reconstruction of 3D Objects --- p.29Chapter 3.6 --- Experimental Results --- p.32Chapter 3.7 --- Summary --- p.32Chapter 4 --- Curved Objects Reconstruction from 2D Line Drawings --- p.35Chapter 4.1 --- Introduction --- p.35Chapter 4.2 --- Related Work --- p.36Chapter 4.2.1 --- Face Identification --- p.36Chapter 4.2.2 --- 3D Reconstruction of planar objects --- p.37Chapter 4.3 --- Reconstruction of Curved Objects --- p.37Chapter 4.3.1 --- Transformation of Line Drawings --- p.37Chapter 4.3.2 --- Finding 3D Bezier Curves --- p.39Chapter 4.3.3 --- Bezier Surface Patches and Boundaries --- p.40Chapter 4.3.4 --- Generating Bezier Surface Patches --- p.41Chapter 4.4 --- Results --- p.43Chapter 4.5 --- Summary --- p.45Chapter 5 --- Planar Limbs and Degen Generalized Cylinders --- p.47Chapter 5.1 --- Introduction --- p.47Chapter 5.2 --- Planar Limbs and View Directions --- p.49Chapter 5.3 --- DGCs in Homogeneous Coordinates --- p.53Chapter 5.3.1 --- Homogeneous Coordinates --- p.53Chapter 5.3.2 --- Degen Surfaces --- p.54Chapter 5.3.3 --- DGCs --- p.54Chapter 5.4 --- Properties of DGCs --- p.56Chapter 5.5 --- Potential Applications --- p.59Chapter 5.5.1 --- Recovery of DGC Descriptions --- p.59Chapter 5.5.2 --- Deformable DGCs --- p.60Chapter 5.6 --- Summary --- p.61Chapter 6 --- Conclusion and Future Work --- p.62Bibliography --- p.6

From on-line sketching to 2D and 3D geometry: A fuzzy knowledge based system

The paper describes the development of a fuzzy knowledge based prototype system for conceptual design. This real time system is designed to infer user’s sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles, arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tools, but also accepts user’s interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry

From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an overview of the supplementary material available at multiview-3d-drawing.sourceforge.ne

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral

Sketching space

In this paper, we present a sketch modelling system which we call Stilton. The program resembles a desktop VRML browser, allowing a user to navigate a three-dimensional model in a perspective projection, or panoramic photographs, which the program maps onto the scene as a `floor' and `walls'. We place an imaginary two-dimensional drawing plane in front of the user, and any geometric information that user sketches onto this plane may be reconstructed to form solid objects through an optimization process. We show how the system can be used to reconstruct geometry from panoramic images, or to add new objects to an existing model. While panoramic imaging can greatly assist with some aspects of site familiarization and qualitative assessment of a site, without the addition of some foreground geometry they offer only limited utility in a design context. Therefore, we suggest that the system may be of use in `just-in-time' CAD recovery of complex environments, such as shop floors, or construction sites, by recovering objects through sketched overlays, where other methods such as automatic line-retrieval may be impossible. The result of using the system in this manner is the `sketching of space' - sketching out a volume around the user - and once the geometry has been recovered, the designer is free to quickly sketch design ideas into the newly constructed context, or analyze the space around them. Although end-user trials have not, as yet, been undertaken we believe that this implementation may afford a user-interface that is both accessible and robust, and that the rapid growth of pen-computing devices will further stimulate activity in this area

Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively