A Silhouette-Based Algorithm for Texture Registration and Stitching

Lensch, Hendrik P. A.; Heidrich, Wolfgang; Seidel, Hans‐Peter

doi:10.1006/gmod.2001.0554

Cited by 111 publications

(73 citation statements)

References 20 publications

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“…This task is regarded as an optimization problem. The subject's silhouettes, as seen from the different camera viewpoints, are used to match the model to the video images (an idea used in similar form in [22]): The model is rendered from all camera viewpoints, and the rendered images are thresholded to yield binary masks of the model's silhouettes. The rendered model silhouettes are then compared to the corresponding image silhouettes [6,43,42,45,44].…”

Section: Silhouette-based Analysis-through-synthesismentioning

confidence: 99%

Video-based Capturing and Rendering of People

2008

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Abstract. The interesting combination of Chapters in this book gives a nice overview of the importance of the analysis of human motion in three different scientific disciplines, computer vision, biomechanics, and computer graphics. In the computer vision field the main goal has been to develop robust and efficient techniques for capturing and measuring human motion. Researchers from biomechanics, on the other hand, try to capitalize on such measurement methods to analyze the characteristics of human motion. In contrast, the focus of computer graphics research has been to realistically animate and render moving humans. In this Chapter, we will outline that a joint solution to the acquisition, the reconstruction, and the rendering problem paves the trail for video-based capturing and rendering of people. We present a model-based system to create realistic 3D videos of human actors that puts this idea into practice. Our method enables us to capture human actors with multiple video cameras and to render their performances in real-time from arbitrary novel viewpoints and under arbitrary novel lighting conditions.

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Section: Silhouette-based Analysis-through-synthesismentioning

confidence: 99%

Video-based Capturing and Rendering of People

2008

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“…The same rendering pipeline is used again to project the visual hull mesh onto the images, which are compared with the ground-truth silhouettes. The error measurement adopted for comparison is modified from the silhouette cost function presented in (Lensch et al, 2001), taking both the miss and false alarm cases into account:…”

Section: Quantitative Comparisonsmentioning

confidence: 99%

Exact Visual Hull From Marching Cubes

Chen

Wong

2008

Proceedings of the Third International Conference on Computer Vision Theory and Applications

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Abstract:The marching cubes algorithm has been widely adopted for extracting a surface mesh from a volumetric description of the visual hull reconstructed from silhouettes. However, typical volumetric descriptions, such as an octree, provide only a binary description about the visual hull. The lack of interpolation information along each voxel edge, which is required by the marching cubes algorithm, usually results in inaccurate and bumpy surface mesh. In this paper, we propose a novel method to efficiently estimate the exact intersections between voxel edges and the visual hull boundary, which replace the missing interpolation information. The method improves both the visual quality and accuracy of the estimated visual hull mesh, while retaining the simplicity and robustness of the volumetric approach. To verify this claim, we present both synthetic and real-world experiments, as well as comparisons with existing volumetric approaches and other approaches targeting at an exact visual hull reconstruction.

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“…Lensch [9], [8], [10] proposed a silhouette-based approach. The size of XOR regions of silhouette image of a 2D image and a projected 3D model is defined as the similarity measure, and the optimum pose which minimizes the size of the XOR regions is determined using the Downhill Simplex method.…”

Section: Related Workmentioning

confidence: 99%