Shape from silhouette using topology-adaptive mesh deformation

We present a computationally efficient and robust shape from silhouette method based on topology-adaptive mesh deformation, which can produce accurate, smooth, and topologically consistent 3D mesh models of complex real objects. The deformation scheme is based on the conventional snake model coupled with local mesh transform operations that control the resolution and uniformity of the deformable mesh. Based on minimum and maximum edge length constraints imposed on the mesh, we describe a fast collision detection method which is crucial for computational efficiency of the reconstruction process. The topology of the deformable mesh, which is initially zero genus, can be modified whenever necessary by merging operations in a controlled and robust manner by exploiting the topology information available in the silhouette images. The performance of the proposed shape from silhouette technique is demonstrated on several real objects.


Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation
Sahillioğlu, Yusuf; Yemez, Y. (2010-03-01)
We present a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking...
Silhouette-based 3d reconstruction by energy minimization
Mengi, Emre; Atalay, Mehmet Volkan; Department of Computer Engineering (2003)
To generate a model of an object in the framework of silhouette based recon struction from multiple views, various energy minimization techniques with dif ferent surface representations are used. Initially, original 3D snakes with layered parametric representation is applied. Snake is deformed to minimize an energy functional imposing smoothness and attraction towards 3D surface points de tected from 2D images. Secondly, a representation in spherical coordinates is suggested and the definition of the energy...
Keyframe based bi directional 2 D mesh representation for video object tracking and manipulation
Eren, Pekin Erhan (1999-10-28)
We propose a new bi-directional 2-D mesh representation of video objects, which utilizes multiple keyframes with forward and backward tracking. Experimental results on use of this representation for video object tracking in the presence of self occlusion are presented.
Bi-directional 2-D mesh representation for video object rendering, editing and superresolution in the presence of occlusion
Eren, Pekin Erhan; Tekalp, AM (2003-05-01)
In this paper, we propose a new bi-directional 2-D mesh representation of video objects, which utilizes forward and backward reference frames (keyframes). This framework extends the previous uni-directional mesh representation to enable efficient rendering, editing, and superresolution of video objects in the presence of occlusion by allowing bidirectional texture mapping as in MPEG B-frames. The video object of interest is tracked between two successive keyframes (which can be automatically or interactivel...
Detail-Preserving Mesh Unfolding for Nonrigid Shape Retrieval
Sahillioğlu, Yusuf (2016-06-01)
We present a shape deformation algorithm that unfolds any given 3D shape into a canonical pose that is invariant to nonrigid transformations. Unlike classical approaches, such as least-squares multidimensional scaling, we preserve the geometric details of the input shape in the resulting shape, which in turn leads to a content-based nonrigid shape retrieval application with higher accuracy. Our optimization framework, fed with a triangular or a tetrahedral mesh in 3D, tries to move each vertex as far away f...
Citation Formats
Y. Yemez and Y. Sahillioğlu, “Shape from silhouette using topology-adaptive mesh deformation,” PATTERN RECOGNITION LETTERS, vol. 30, no. 13, pp. 1198–1207, 2009, Accessed: 00, 2022. [Online]. Available: