This paper presents an unsupervised imagesegmentation approach for obtaining a set of silhouettes along with the visual hull (VH) of an object observed from multiple viewpoints. The proposed approach can deal with mo...
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This paper presents an unsupervised imagesegmentation approach for obtaining a set of silhouettes along with the visual hull (VH) of an object observed from multiple viewpoints. The proposed approach can deal with mostly any type of appearance characteristics such as texture, similar background color, shininess, transparency besides other phenomena such as shadows and color bleeding. Compared to more classical methods for silhouette extraction from multiple views, for which certain assumptions are made on the object or scene, neither the background nor the object appearance properties are modeled. The only assumption is the constancy of the unknown background for a given camera viewpoint while the object is under motion. The principal idea of the method is the estimation of the temporal evolution of each pixel over time which provides a stability measurement and leads to its associated background likelihood. In order to cope with shadows and self-shadows, an object is captured under different lighting conditions. Furthermore, the information from the space, time and lighting domains is exploited and merged based on a MRF framework and the constructed energy function is minimized via graph cut. Experiments are performed on a light stage where the object is set on a turntable and is observed from calibrated viewpoints on a hemisphere around the object. Real data experiments show that the proposed approach allows for robust and efficient VH reconstruction of a variety of challenging objects. (C) 2014 Elsevier Inc. All rights reserved.
Despite the great progress on interactive imagesegmentation, image co-segmentation, 2D and 3D segmentation, there is still no workable solution to the problem: given a set of calibrated or un-calibrated multi-view im...
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Despite the great progress on interactive imagesegmentation, image co-segmentation, 2D and 3D segmentation, there is still no workable solution to the problem: given a set of calibrated or un-calibrated multi-viewimages (say, more than 40 images), by interactively cutting 3 similar to 4 images, can the foreground object of the rest images be quickly cutout automatically and accurately? In this paper, we propose a non-trivial engineering solution to this problem. Our basic idea is to integrate 3D segmentation with 2D segmentation so as to combine their advantages. Our proposed system iteratively performs 2D and 3D segmentation, where the 3D segmentation results are used to initialize 2D segmentation and ensure the silhouette consistency among different views and the 2D segmentation results are used to provide more accurate cues for the 3D segmentation. The experimental results show that the proposed system is able to generate highly accurate segmentation results, even for some challenging real-world multi-viewimage sequences, with a small amount of user input. (c) 2013 Elsevier Inc. All rights reserved.
fThis paper presents an imagesegmentation approach for obtaining a set of silhouettes along with the Visual Hull of an object observed from multiple viewpoints. The proposed approach can deal with mostly any type of ...
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ISBN:
(纸本)9789897581335
fThis paper presents an imagesegmentation approach for obtaining a set of silhouettes along with the Visual Hull of an object observed from multiple viewpoints. The proposed approach can deal with mostly any type of appearance characteristics such as textured or textureless, shiny or lambertian surface reflectance, opaque or transparent objects. Compared to more classical methods for silhouette extraction from multiple views, for which certain assumptions are made on the object or scene, neither the background nor the object's appearance properties are modeled. The only assumption is the constancy of the unknown background at a given camera viewpoint while the object is under motion. The principal idea of the method is the estimation of the temporal evolution of each pixel over time which leads to the ability to estimate the background likelihood. Furthermore, the object is captured under different lighting conditions in order to cope with shadows. All the information from the space, time and lighting domains is merged based on a MRF framework and the constructed energy function is minimized via graph cuts.
In this paper a method for the reconstruction of an objects Visual Hull (VH) is presented. An image sequence of a moving object under different lighting condition is captured and analyzed. In this analysis, informatio...
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ISBN:
(纸本)9783319251172;9783319251165
In this paper a method for the reconstruction of an objects Visual Hull (VH) is presented. An image sequence of a moving object under different lighting condition is captured and analyzed. In this analysis, information from multiple domains (space, time and lighting) is merged based on a MRF framework. The advantage of the proposed method is that it allows to obtain an approximation of an object 3D model without any assumption on object appearance or geometry. Real-data experiments show that the proposed approach allows for robust VH reconstruction of a variety of challenging objects such as a transparent wine glass or a light bulb.
Existing architecture semantic modeling methods in 3D complex urban scenes continue facing difficulties, such as limited training data, lack of semantic information, and inflexible model processing. Focusing on extrac...
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Existing architecture semantic modeling methods in 3D complex urban scenes continue facing difficulties, such as limited training data, lack of semantic information, and inflexible model processing. Focusing on extracting and adopting accurate semantic information into a modeling process, this work presents a framework for lightweight modeling of buildings that joints point clouds semantic segmentation and 3D feature line detection constrained by geometric and photometric consistency. The main steps are: (1) Extraction of single buildings from point clouds using 2D-3D semi-supervised semantic segmentation under photometric and geometric constraints. (2) Generation of lightweight building models by using 3D plane-constrained multi-view feature line extraction and optimization. (3) Introduction of detailed semantics of building elements into independent 3D building models by using fine-grained segmentation of multi-viewimages to achieve high-accuracy architecture lightweight modeling with fine-grained semantic information. Experimental results demonstrate that it can perform independent lightweight modeling of each building on point cloud at various scales and scenes, with accurate geometric appearance details and realistic textures. It also enables independent processing and analysis of each building in the scenario, making them more useful in practical applications.
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