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

Exploiting artistic cues to obtain line labels for free-hand sketches

Artistic cues help designers to communicate design intent in sketches. In this paper, we show how these artistic cues may be used to obtain a line labelling interpretation of freehand sketches, using a cue-based genetic algorithm to obtain a labelling solution that matches design intent. In the paper, we show how this can be achieved from off-line or paper based sketches, thereby allowing designers greater flexibility in the choice of sketching medium.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

Sketch-based modeling with a differentiable renderer

© 2020 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons, Ltd. Sketch-based modeling aims to recover three-dimensional (3D) shape from two-dimensional line drawings. However, due to the sparsity and ambiguity of the sketch, it is extremely challenging for computers to interpret line drawings of physical objects. Most conventional systems are restricted to specific scenarios such as recovering for specific shapes, which are not conducive to generalize. Recent progress of deep learning methods have sparked new ideas for solving computer vision and pattern recognition issues. In this work, we present an end-to-end learning framework to predict 3D shape from line drawings. Our approach is based on a two-steps strategy, it converts the sketch image to its normal image, then recover the 3D shape subsequently. A differentiable renderer is proposed and incorporated into this framework, it allows the integration of the rendering pipeline with neural networks. Experimental results show our method outperforms the state-of-art, which demonstrates that our framework is able to cope with the challenges in single sketch-based 3D shape modeling

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

On Computer Stereo Vision with Wire Frame Models

Coordinated Science Laboratory changed its name from Control Systems LaboratoryShould have been numbered UILU-ENG 77-2252, and that number may have been distributed on some copies.Joint Services Electronics Program / DAAB-07-72-C-0259Ope

Sensory processing and world modeling for an active ranging device

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented

Fast visual recognition of large object sets

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1990.Includes bibliographical references (leaves 117-123).by Michael Joseph Villalba.Ph.D

Towards Scene Understanding with Detailed 3D Object Representations

Current approaches to semantic image and scene understanding typically employ rather simple object representations such as 2D or 3D bounding boxes. While such coarse models are robust and allow for reliable object detection, they discard much of the information about objects' 3D shape and pose, and thus do not lend themselves well to higher-level reasoning. Here, we propose to base scene understanding on a high-resolution object representation. An object class - in our case cars - is modeled as a deformable 3D wireframe, which enables fine-grained modeling at the level of individual vertices and faces. We augment that model to explicitly include vertex-level occlusion, and embed all instances in a common coordinate frame, in order to infer and exploit object-object interactions. Specifically, from a single view we jointly estimate the shapes and poses of multiple objects in a common 3D frame. A ground plane in that frame is estimated by consensus among different objects, which significantly stabilizes monocular 3D pose estimation. The fine-grained model, in conjunction with the explicit 3D scene model, further allows one to infer part-level occlusions between the modeled objects, as well as occlusions by other, unmodeled scene elements. To demonstrate the benefits of such detailed object class models in the context of scene understanding we systematically evaluate our approach on the challenging KITTI street scene dataset. The experiments show that the model's ability to utilize image evidence at the level of individual parts improves monocular 3D pose estimation w.r.t. both location and (continuous) viewpoint.Comment: International Journal of Computer Vision (appeared online on 4 November 2014). Online version: http://link.springer.com/article/10.1007/s11263-014-0780-