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A TwoStage Algorithm for Planning the Next View From Range Images
 In Proc 6th British Machine Vision Conference
, 1998
"... A new technique is presented for determining the positions where a range sensor should be located to acquire the surfaces of a complex scene. The algorithm consists of two stages. The first stage applies a voting scheme that considers occlusion edges. Most of the surfaces of the scene are recover ..."
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A new technique is presented for determining the positions where a range sensor should be located to acquire the surfaces of a complex scene. The algorithm consists of two stages. The first stage applies a voting scheme that considers occlusion edges. Most of the surfaces of the scene are recovered through views computed in that way. Then, the second stage fills up remaining holes through a scheme based on visibility analysis. By leaving the more expensive visibility computations at the end of the exploration process, efficiency is increased. 1 Introduction The automatic reconstruction of 3D objects (scenes in general) through range images is gaining popularity in computer vision and robotics owing to the variety of applications that can benefit from it, including world modeling [2], reverse engineering and object segmentation [3] or recognition. Two basic tasks must be addressed in order to solve that problem. First, an exploration process is necessary for determining the pos...
Towards Automatic Interpolation for Real and Distant Image Pairs
, 1999
"... Imagebased rendering offers the advantage of being able to provide realistic output and at the same time to avoid the difficult problem of a complete geometric and photometric modeling of the real world. The method described here is able to deal with non rigid scenes and large camera motions. We p ..."
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Cited by 6 (3 self)
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Imagebased rendering offers the advantage of being able to provide realistic output and at the same time to avoid the difficult problem of a complete geometric and photometric modeling of the real world. The method described here is able to deal with non rigid scenes and large camera motions. We present in this report a three step algorithm for the interpolation of two views of a scene, from which we can for instance simulated a camera motion withing the given scene. The first step establishes pixel correspondences between the images and is the most difficult part. We justify the choice of regiongrowing based dense matching methods and we summarize their principle. Secondly, a robust algorithm converts these pixel correspondences to an adequate structure for the last step: image interpolation. This structure encompasses the transformation between the images using constrained and dependent triangulations in both of them, and handles the halfoccluded areas. The implementation of th...
Autonomous Sensor Planning for 3D Reconstruction of Complex Objects from Range Images
, 1998
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Incremental Multiview Integration of Range Images
"... This paper presents a new method for the incremental integration of overlapped range images. It is assumed that frame transformations between all pairs of views can be reliably computed. This method proceeds by progressively merging each new sensed range image with the current reconstructed model. T ..."
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This paper presents a new method for the incremental integration of overlapped range images. It is assumed that frame transformations between all pairs of views can be reliably computed. This method proceeds by progressively merging each new sensed range image with the current reconstructed model. The proposed method consists of three stages. In the first stage the overlapped regions are identified. Then, from the overlapped regions, the points that define the integration boundaries are projected over a reference plane and are triangulated over that 2D space by means of a constrained Delaunay algorithm. Finally the obtained triangulation is backprojected to the 3D range image space. These new triangular meshes represent the sewing between the meshes to be
Modeling Range Images with Bounded Error Triangular Meshes without Optimization
"... This paper presents a new technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This new approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point ..."
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This paper presents a new technique for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This new approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 3D point defined in a reference frame associated with the range sensor. Then, those 3D points are mapped to a 3D curvature space. In the second stage, the points contained in this curvature space are triangulated through a 3D Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this
Incremental Multiview Integration of Range Images
"... This paper presents a new method for the incremental integration of overlapped range images. It is assumed that frame transformations between all pairs of views can be reliably computed. This method proceeds by progressively merging each new sensed range image with the current reconstructed model. ..."
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This paper presents a new method for the incremental integration of overlapped range images. It is assumed that frame transformations between all pairs of views can be reliably computed. This method proceeds by progressively merging each new sensed range image with the current reconstructed model. The proposed method consists of three stages. In the first stage the overlapped regions are identified. Then, from the overlapped regions, the points that define the integration boundaries are projected over a reference plane and are triangulated over that 2 0 space by means of a constrained Delaunay algorithm. Finally the obtained triangulation is backprojected to the 3 0 range image space. These new triangular meshes represent the sewing between the meshes to be integrated. In this way a new single triangular mesh which will be used to merge with futures range images is generated. Experimental results are presented. 1.
Approximation and Processing of Intensity Images with DiscontinuityPreserving Adaptive Triangular Meshes
"... Abstract. A new algorithm for approximating intensity images with adaptive triangular meshes keeping image discontinuities and avoiding optimization is presented. The algorithm consists of two main stages. In the first stage, the original image is adaptively sampled at a set of points, taking into a ..."
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Abstract. A new algorithm for approximating intensity images with adaptive triangular meshes keeping image discontinuities and avoiding optimization is presented. The algorithm consists of two main stages. In the first stage, the original image is adaptively sampled at a set of points, taking into account both image discontinuities and curvatures. In the second stage, the sampled points are triangulated by applying a constrained 2D Delaunay algorithm. The obtained triangular meshes are compact representations that model the regions and discontinuities present in the original image with many fewer points. Thus, image processing operations applied upon those meshes can perform faster than upon the original images. As an example, four simple operations (translation, rotation, scaling and deformation) have been implemented in the 3D geometric domain and compared to their image domain counterparts.1 1
Modeling Range Images with Bounded Error Triangular Meshes without Optimization
"... This paper presents a new technique,for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This new approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 30 poin ..."
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This paper presents a new technique,for approximating range images by means of adaptive triangular meshes with a bounded approximation error and without applying optimization. This new approach consists of three stages. In the first stage, every pixel of the given range image is mapped to a 30 point defined in a reference frame associated with the range sensor. Then, those 30 points are mapped to a 3 0 curvature space. In the second stage, the points contained in this curvature space are triangulated through a 30 Delaunay algorithm, giving rise to a tetrahedronization of them. In the last stage, an iterative process starts digging the external surface of the previous tetrahedronization, removing those triangles that do not fulfill the given approximation error. In this way, successive fronts of triangular meshes are obtained in both range image space and curvature space. This iterative process is applied until a triangular mesh in the range image space fulfilling the given approximation error is obtained. Experimental results are presented. 1.
Author manuscript, published in "Proceedings of the 12th Conference on Vision Interface, VI99 (1999)" Joint View Triangulation for Two Views
"... Related Work Other structures have been proposed to model visibility information in computer vision and computer graphics (e.g. aspect graphs [6] and visibility skeleton [2]), but they need a rigid 3D model as input and are not optimized for the same uses. In contrast to these, our strucinria005901 ..."
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Related Work Other structures have been proposed to model visibility information in computer vision and computer graphics (e.g. aspect graphs [6] and visibility skeleton [2]), but they need a rigid 3D model as input and are not optimized for the same uses. In contrast to these, our strucinria00590116,
Joint View Triangulation for Two Views
"... We propose the Joint View Triangulation, which coherently models all visible and partially occluded patches within views of a scene (rigid or not). It is built from an underlying dense matching and can be used for any application requiring discrete and efficient representation of deformation and dis ..."
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We propose the Joint View Triangulation, which coherently models all visible and partially occluded patches within views of a scene (rigid or not). It is built from an underlying dense matching and can be used for any application requiring discrete and efficient representation of deformation and displacement between views. First robustness has to deal the unavoidable matching errors. Secondly matched and half occluded areas should be separated in each view to allow different processes on them. Finally, the elements of the structure which represent the matched area of each view pair should be in correspondence. This ensures a global coherence of the data and avoid redundant processes. In fact, we merely expect to an approximate but coherent structure, because of the finite precision of the images and bad matches. This paper deals only with the two view case but also applies the joint view triangulation to morphing between real image pairs with large camera displacement. 3. For each pair of views, a correspondence between primitives which represents the common (i.e. matched) areas of the pair. This ensures the global coherence of the data and avoids redundant processing during use. Figure 1 shows an example of a JVT. A precise definition is given in the next section. Section 3 presents a robust algorithm for its construction in the two view case. Sections 4 and 5 apply it to the morphing of real image pairs, which are matched with a regiongrowing method. A report [13] describe the complete process (matching, JVT and warping).