Reconstruction of scene models from sparse 3D structure

Manessis, A.; Hilton, Adrian; Palmer, Phil; McLauchlan, Philip F.; Shen, Xinquan

doi:10.1109/cvpr.2000.854938

Cited by 23 publications

(11 citation statements)

References 8 publications

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“…In our final area of computer vision in consideration, surface from contour completion [32,25,40,49], we again see the requirement for additional knowledge moving us away from the generalised volume completion problem. Here, however, unlike earlier knowledge dependent completion approaches we foresee potential for using surface/contour relatability as a sub-part of generalised volume completion where the required constraining knowledge is itself derived from the other identified aspects of volume mergability, world knowledge, and pattern completion.…”

Section: Discussionmentioning

confidence: 99%

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Amodal volume completion: 3D visual completion

Breckon

Fisher

2005

Computer Vision and Image Understanding

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This work considers the common problem of completing partially visible artifacts within a 3D scene. Human vision abilities to complete such artifacts are well studied within the realms of perceptual psychology. However, the psychological explanations for completion have received only limited application in the domain of 3D computer vision. Here, we examine prior work in this area of computer vision with reference to psychological accounts of completion and identify remaining challenges for future work.

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Section: Discussionmentioning

confidence: 99%

“…Recent work in constructing 3D models from sparse 3D data [49] has utilised triangulation on sparse data sets obtained using structure-from-motion over a sequence of 2D images. In this work, sparse corner and edge features were used as primes for the construction of a triangular mesh to describe the surfaces of the scene.…”

Section: Surfaces From Sparse Datamentioning

confidence: 99%

Amodal volume completion: 3D visual completion

Breckon

Fisher

2005

Computer Vision and Image Understanding

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“…These depth maps can be merged as a post process stage (Narayanan et al, 1998). The fourth class is composed by algorithms that, instead of performing dense matching for each pixel, extract and match a subset of feature points for each image and then fit a surface to the reconstructed features (Manessis et al, 2000, Taylor, 2003.…”

Section: Previous Workmentioning

confidence: 99%

Accurate Multiview Stereo Reconstruction With Fast Visibility Integration and Tight Disparity Bounding

Toldo¹,

Fantini²,

Giona³

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A novel multi-view stereo reconstruction method is presented. The algorithm is focused on accuracy and it is highly engineered with some parts taking advantage of the graphics processing unit. In addition, it is seamlessly integrated with the output of a structure and motion pipeline. In the first part of the algorithm a depth map is extracted independently for each image. The final depth map is generated from the depth hypothesis using a Markov random field optimization technique over the image grid. An octree data structure accumulates the votes coming from each depth map. A novel procedure to remove rogue points is proposed that takes into account the visibility information and the matching score of each point. Finally a texture map is built by wisely making use of both the visibility and the view angle informations. Several results show the effectiveness of the algorithm under different working scenarios.

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“…Other, more complicated, visibility constraints are also used and found to be necessary in practice: for example, a surface patch that partially occludes another one, without occluding an actual 3D point, is rejected. Such visibility constraints were used in [7], were a surface mesh is built incrementally, starting with a mesh obtained by a Delaunay triangulation in one view, and then rejecting and adding triangles based on visibility constraints of one additional view after the other. We proceed differently, by iteratively adding new triangles to manually or automatically selected seed triangles, and thus by letting a mesh grow, directly ensuring all available visibility constraints (and other constraints, see below).…”

Section: D Registration and Mesh Generationmentioning

confidence: 99%

VISIRE: photorealistic 3D reconstruction from video sequences

Rodríguez¹,

Sturm

Wilczkowiak

et al.

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)

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Traditionally, building 3D reconstructions of large scenarios such as a museum or historical site has been costly, time consuming and required the contribution of expert personnel. Usually the results showed an artificial look and had little interactivity. However, newly developed technologies in the areas of video analysis, camera calibration and texture fusion allow us to think in a much more satisfying scenario where the user with the only aid of a domestic video camera is able to acquire all the information it is required to construct the 3D model of the desired environment in an easy and comfortable manner.In this paper, the results obtained in the EC funded project VISIRE are presented. VISIRE attempts to construct photorealistic 3D models of large scenarios using as input multiple freehand video sequences. Once acquired, the computer vision software will process the video information off-line in order to obtain the 3D mesh together with the textures required to obtain a 3D model highly resembling the original.

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Reconstruction of scene models from sparse 3D structure

Cited by 23 publications

References 8 publications

Amodal volume completion: 3D visual completion

Amodal volume completion: 3D visual completion

Accurate Multiview Stereo Reconstruction With Fast Visibility Integration and Tight Disparity Bounding

VISIRE: photorealistic 3D reconstruction from video sequences

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