Separation of line drawings based on split faces for 3D object reconstruction

© 2014 IEEE. Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications. If the line drawing of a complex 3D object is decomposed into primitives of simple shape, the object can be easily reconstructed. We propose an effective method to conduct the line drawing separation and turn a complex line drawing into parametric 3D models. This is achieved by recursively separating the line drawing using two types of split faces. Our experiments show that the proposed separation method can generate more basic and simple line drawings, and its combination with the example-based reconstruction can robustly recover wider range of complex parametric 3D objects than previous methods.This work was supported by grants from Science, Industry, Trade, and Information Technology Commission of Shenzhen Municipality (No. JC201005270378A), Guangdong Innovative Research Team Program (No. 201001D0104648280), Shenzhen Basic Research Program (JCYJ20120617114614438, JC201005270350A, JCYJ20120903092050890), Scientific Research Fund of Hunan Provincial Education Department (No. 13C073), Industrial Technology Research and Development Program of Hengyang Science and Technology Bureau (No.2013KG75), and the Construct Program of the Key Discipline in Hunan Provinc

Exploring local regularities for 3D object recognition

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness

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

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

An evolutionary approach to determining hidden lines from a natural sketch

This paper focuses on the identification of hidden lines and junctions from natural sketches of drawings that exhibit an extended-trihedral geometry. Identification of hidden lines and junctions is essential in the creation of a complete 3D model of the sketched object, allowing the interpretation algorithms to infer what the unsketched back of the object should look like. This approach first labels the sketched visible edges of the object with a geometric edge label, obtaining a labelled junction at each of the visible junctions of the object. Using a dictionary of junctions with visible and hidden edges, these labelled visible junctions are then used to deduce the edge interpretation and orientation of some of the hidden edges. A genetic algorithm is used to combine these hidden edges into hidden junctions, evolving the representation of the hidden edges and junctions until a feasible hidden view representation of the object is obtained.peer-reviewe

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

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