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首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >A Point-Cloud-Based Multiview Stereo Algorithm for Free-Viewpoint Video
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A Point-Cloud-Based Multiview Stereo Algorithm for Free-Viewpoint Video

机译：基于点云的多视点立体视频自由视点算法

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摘要

This paper presents a robust multiview stereo (MVS) algorithm for free-viewpoint video. Our MVS scheme is totally point-cloud-based and consists of three stages: point cloud extraction, merging, and meshing. To guarantee reconstruction accuracy, point clouds are first extracted according to a stereo matching metric which is robust to noise, occlusion, and lack of texture. Visual hull information, frontier points, and implicit points are then detected and fused with point fidelity information in the merging and meshing steps. All aspects of our method are designed to counteract potential challenges in MVS data sets for accurate and complete model reconstruction. Experimental results demonstrate that our technique produces the most competitive performance among current algorithms under sparse viewpoint setups according to both static and motion MVS data sets.
机译：本文提出了一种针对自由视点视频的健壮的多视点立体声（MVS）算法。我们的MVS方案完全基于点云，包括三个阶段：点云提取，合并和网格划分。为了保证重建精度，首先根据立体匹配度量提取点云，该立体声匹配度量对噪声，遮挡和缺乏纹理具有鲁棒性。然后在合并和网格划分步骤中检测视觉船体信息，边界点和隐含点，并将其与点保真度信息融合。我们的方法的各个方面都旨在应对MVS数据集中的潜在挑战，以进行准确而完整的模型重建。实验结果表明，在基于静态和运动MVS数据集的稀疏视点设置下，我们的技术在当前算法中产生了最具竞争力的性能。

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