Article

Silhouette and stereo fusion for 3D object modeling

Signal and Image Processing Department, CNRS UMR 5141, Ecole Nationale Supérieure des Télécommunications, France
Computer Vision and Image Understanding (Impact Factor: 1.36). 01/2003; 96(3):367-392. DOI: 10.1016/j.cviu.2004.03.016
Source: DBLP

ABSTRACT In this paper, we present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multi-stereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multi-grid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the contour generators of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.

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    • "This is a functional that penalizes solutions that do not respect prior assumptions, and plays a key role both in the quality of the reconstruction, as well as in the efficiency of the numerical optimization scheme. The most principled approaches to 3-d reconstruction aim to infer a collection of (multiply-connected, piecewise smooth) surfaces directly, represented intrinsically without regards to the images [2] [10] [18] [28] [38] [21] [42], as evident by the large body of literature on shape space and shape optimization. In these methods, both the geometry and the topology is then inferred to fit the available images. "
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 01/2015
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    • "In shape-from-silhouettes, a set of silhouettes extracted from images is used to model the 3D scene by generating the convex hull produced by a union of projection cones [1] [2]. An energy function used both texture and silhouettes for guiding a deformable model in [10] for single 3D object representation. The methodology described in this paper aims to robustly enforce the consistency of scenes with multiple objects with their corresponding contours segmented from images. "
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    ABSTRACT: This paper proposes enforcing the consistency with segmented contours when modelling scenes with multiple objects from multi-view images. A certain rough initialization of the 3D scene is assumed to be available and in the case of multiple objects inconsistencies are expected. In the proposed shape-from-contours approach images are segmented and back-projections of segmented contours are used for enforcing the consistency of the segmented contours with 3D objects from the scene. We provide a study for the physical requirements for detecting occlusions when reconstructing 3-D scenes with multiple objects.
    IEEE International Conference on Image Processing, Paris, France; 10/2014
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    • "Most of these procedures apply consistency measures only to single stereopairs, while geometric constraints are applied only during the fusion of the point clouds derived by the stereopairs or via some volumetric approaches. The most common multi-view algorithms rely on silhouette and fusion (Hern andez Esteban and Schmitt, 2004), volumetric graph-cut (Vogiatzis et al., 2007), patch-based methods (Furukawa and Ponce, 2010) and global optimisation (Vu et al., 2012). Most of the proposed matching methods are based on similarity or photo-consistency measures; in other words, they compare pixel values between the images. "
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    ABSTRACT: Image matching has a history of more than 50 years, with the first experiments performed with analogue procedures for cartographic and mapping purposes. The recent integration of computer vision algorithms and photogrammetric methods is leading to interesting procedures which have increasingly automated the entire image-based 3D modelling process. Image matching is one of the key steps in 3D modelling and mapping. This paper presents a critical review and analysis of four dense image-matching algorithms, available as open-source and commercial software, for the generation of dense point clouds. The eight datasets employed include scenes recorded from terrestrial and aerial blocks, acquired with convergent and normal (parallel axes) images, and with different scales. Geometric analyses are reported in which the point clouds produced with each of the different algorithms are compared with one another and also to ground-truth data.
    The Photogrammetric Record 06/2014; 29(146-146):144-166. DOI:10.1111/phor.12063 · 1.38 Impact Factor
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