We propose a 3D model feature line extraction method using templates for guidance. The 3D model is first projected into a depth map, and a set of candidate feature points are extracted. Then, a conditional random fiel...We propose a 3D model feature line extraction method using templates for guidance. The 3D model is first projected into a depth map, and a set of candidate feature points are extracted. Then, a conditional random fields (CRF) model is established to match the sketch points and the candidate feature points. Using sketch strokes, the candidate feature points can then be connected to obtain the feature lines, and using a CRF-matching model, the 2D image shape similarity features and 3D model geometric features can be effectively integrated. Finally, a relational metric based on shape and topological similarity is proposed to evaluate the matching results, and an iterative matching process is applied to obtain the globally optimized model feature lines. Experimental results showed that the proposed method can extract sound 3D model feature lines which correspond to the initial sketch template.展开更多
This paper presents a synthesis method for 3D models using Petri net. Feature structure units from the example model are extracted, along with their constraints, through structure analysis, to create a new model using...This paper presents a synthesis method for 3D models using Petri net. Feature structure units from the example model are extracted, along with their constraints, through structure analysis, to create a new model using an inference method based on Petri net. Our method has two main advantages: first, 3D model pieces are delineated as the feature structure units and Petri net is used to record their shape features and their constraints in order to outline the model, including extending and deforming operations; second, a construction space generating algorithm is presented to convert the curve drawn by the user into local shape controlling parameters, and the free form deformation (FFD) algorithm is used in the inference process to deform the feature structure units. Experimental results showed that the proposed method can create large-scale complex scenes or models and allow users to effectively control the model result.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 61272219, 61100110, and 61021062)the National High-Tech R&D Program (863) of China (No. 2007AA01Z334)+1 种基金the Program for New Century Excellent Talents in University (No. NCET-0404605)the Science and Technology Program of Jiangsu Province, China (Nos. BE2010072, BE2011058, and BY2012190)
文摘We propose a 3D model feature line extraction method using templates for guidance. The 3D model is first projected into a depth map, and a set of candidate feature points are extracted. Then, a conditional random fields (CRF) model is established to match the sketch points and the candidate feature points. Using sketch strokes, the candidate feature points can then be connected to obtain the feature lines, and using a CRF-matching model, the 2D image shape similarity features and 3D model geometric features can be effectively integrated. Finally, a relational metric based on shape and topological similarity is proposed to evaluate the matching results, and an iterative matching process is applied to obtain the globally optimized model feature lines. Experimental results showed that the proposed method can extract sound 3D model feature lines which correspond to the initial sketch template.
基金supported by the National Natural Science Foundation of China (Nos. 61272219, 61100110, and 61021062)the National High-Tech R&D Program of China (No. 2007AA01Z334)+2 种基金the Program for New Century Excellent Talents in University (No. NCET04-04605)the Science and Technology Program of Jiangsu Province (Nos. BE2010072, BE2011058, and BY2012190)the Graduate Training Innovative Projects Foundation of Jiangsu Province (No. CXLX12_ 0054), China
文摘This paper presents a synthesis method for 3D models using Petri net. Feature structure units from the example model are extracted, along with their constraints, through structure analysis, to create a new model using an inference method based on Petri net. Our method has two main advantages: first, 3D model pieces are delineated as the feature structure units and Petri net is used to record their shape features and their constraints in order to outline the model, including extending and deforming operations; second, a construction space generating algorithm is presented to convert the curve drawn by the user into local shape controlling parameters, and the free form deformation (FFD) algorithm is used in the inference process to deform the feature structure units. Experimental results showed that the proposed method can create large-scale complex scenes or models and allow users to effectively control the model result.