The human vision system can reconstruct a 3D object easily from single 2D line drawings even if the hidden lines of the object are invisible. Now, there are many methods have emulated this ability, but when the hidden...The human vision system can reconstruct a 3D object easily from single 2D line drawings even if the hidden lines of the object are invisible. Now, there are many methods have emulated this ability, but when the hidden lines of the object are invisible, these methods cannot reconstruct a complete 3D object. Therefore, we put forward a new algorithm to settle this hard problem. Our approach consists of two steps： （1） infer the invisible vertices and edges to complete the line drawing, （2） propose a vertex-based optimization method to reconstruct a 3D object.展开更多
An active research topic in computer vision and graphics is developing algorithms that can reconstruct the 3D surface of curved objects from line drawings. There are a number of algorithms have been dedicated to solve...An active research topic in computer vision and graphics is developing algorithms that can reconstruct the 3D surface of curved objects from line drawings. There are a number of algorithms have been dedicated to solve this problem, but they can＇t solve this problem when the geometric structure of a curved object becomes complex. This paper proposes a novel approach to reconstructing a complex curved 3D object from single 2D line drawings. Our approach has three steps： （1） decomposing a complex line drawing into several simpler line drawings and transforming them into polyhedron; （2） reconstructing the 3D wireframe of curved object from these simpler line drawings and generating the curved faces; （3） combining the 3D objects into the complete objects. A number of examples are given to demonstrate the ability of our approach to successfully perform reconstruction of curved objects which are more complex than previous methods.展开更多
It is a research subject in computer vision to 3D reconstruction of an object represented by a single 2D line drawing. Previous works on 3D reconstruction from 2D line drawings focus on objects with lines, plane, view...It is a research subject in computer vision to 3D reconstruction of an object represented by a single 2D line drawing. Previous works on 3D reconstruction from 2D line drawings focus on objects with lines, plane, view, and so on. This paper mainly studies the 3D reconstruction from 2D line drawings. Besides, a new approach is proposed： it is that for the research of the point coordinates of 2D line drawings, so as to achieve the object reconstruction by the reconstruction of point coordinates. The reconstruction process includes： （1） the collection of point coordinates （X,Y） of 2D line drawings; （2） the derivation of mathematical formula about the reconstruction of the point of 2D line drawings, and calculating the corresponding point of the 3D coordinates; （3） the regeneration of 3D graphics with 3D points; （4） analyze error by the proportional of parallel of axonometric projection, in order to prove the accuracy of the method.展开更多
Reverse engineering dealing with images is traditionally based on image processing and contour recognition. A new method is presented based on the combination of sectional slicing with image mosaic. Sectional contours...Reverse engineering dealing with images is traditionally based on image processing and contour recognition. A new method is presented based on the combination of sectional slicing with image mosaic. Sectional contours of the target object are generated by colorful liquid or laser scanning, these images from different views are fused into a set of complete cross-sectional images, thereby the whole practical model is reconstructed in 3D space.展开更多
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 deviat...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.展开更多
文摘The human vision system can reconstruct a 3D object easily from single 2D line drawings even if the hidden lines of the object are invisible. Now, there are many methods have emulated this ability, but when the hidden lines of the object are invisible, these methods cannot reconstruct a complete 3D object. Therefore, we put forward a new algorithm to settle this hard problem. Our approach consists of two steps： （1） infer the invisible vertices and edges to complete the line drawing, （2） propose a vertex-based optimization method to reconstruct a 3D object.
文摘An active research topic in computer vision and graphics is developing algorithms that can reconstruct the 3D surface of curved objects from line drawings. There are a number of algorithms have been dedicated to solve this problem, but they can＇t solve this problem when the geometric structure of a curved object becomes complex. This paper proposes a novel approach to reconstructing a complex curved 3D object from single 2D line drawings. Our approach has three steps： （1） decomposing a complex line drawing into several simpler line drawings and transforming them into polyhedron; （2） reconstructing the 3D wireframe of curved object from these simpler line drawings and generating the curved faces; （3） combining the 3D objects into the complete objects. A number of examples are given to demonstrate the ability of our approach to successfully perform reconstruction of curved objects which are more complex than previous methods.
文摘It is a research subject in computer vision to 3D reconstruction of an object represented by a single 2D line drawing. Previous works on 3D reconstruction from 2D line drawings focus on objects with lines, plane, view, and so on. This paper mainly studies the 3D reconstruction from 2D line drawings. Besides, a new approach is proposed： it is that for the research of the point coordinates of 2D line drawings, so as to achieve the object reconstruction by the reconstruction of point coordinates. The reconstruction process includes： （1） the collection of point coordinates （X,Y） of 2D line drawings; （2） the derivation of mathematical formula about the reconstruction of the point of 2D line drawings, and calculating the corresponding point of the 3D coordinates; （3） the regeneration of 3D graphics with 3D points; （4） analyze error by the proportional of parallel of axonometric projection, in order to prove the accuracy of the method.
基金Supported by Construction of Key Disciplines in Shanghai (B503)
文摘Reverse engineering dealing with images is traditionally based on image processing and contour recognition. A new method is presented based on the combination of sectional slicing with image mosaic. Sectional contours of the target object are generated by colorful liquid or laser scanning, these images from different views are fused into a set of complete cross-sectional images, thereby the whole practical model is reconstructed in 3D space.
文摘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.
