Complete multi-view reconstruction of dynamic scenes from probabilistic fusion of narrow and wide baseline stereo
Complete multi-view reconstruction of dynamic scenes from probabilistic fusion of narrow and wide baseline stereo
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DOI：
10.1109/iccv.2009.5459384
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Tony Tung;S. Nobuhara;T. Matsuyama
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文献类型：
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作者：
Tony Tung;S. Nobuhara;T. Matsuyama
本文提出了一种新的方法来实现准确和完整的动态场景（或三维视频）的多视图重建。3D视频包括由周围的一组摄像机捕获的运动中的3D模型的序列。迄今为止，3D视频重建使用多视图宽基线立体（MVS）重建技术。然而，解决立体对应问题仍然是繁琐的：重建精度福尔斯时，立体照片的一致性是弱的，和完整性受到限制的自遮挡。大多数MVS技术实际上都是为了处理受控环境中的静态对象而设计的，因此无法解决这些问题。因此，我们建议利用每个单视图视频提供的图像内容稳定性来恢复至少一个摄像机可见的任何表面区域。特别是，我们提出了一个原始的概率框架来推导和预测模型的真实表面。我们建议融合多视图结构从运动与强大的3D功能获得MVS，以显着提高重建的完整性和准确性。在最后一步中解决了所有精确特征作为先验的最小切割问题，以重建3D模型。此外，实验结果进行了合成和具有挑战性的真实的世界数据集，以说明我们的方法的鲁棒性和准确性。
This paper presents a novel approach to achieve accurate and complete multi-view reconstruction of dynamic scenes (or 3D videos). 3D videos consist in sequences of 3D models in motion captured by a surrounding set of video cameras. To date 3D videos are reconstructed using multiview wide baseline stereo (MVS) reconstruction techniques. However it is still tedious to solve stereo correspondence problems: reconstruction accuracy falls when stereo photo-consistency is weak, and completeness is limited by self-occlusions. Most MVS techniques were indeed designed to deal with static objects in a controlled environment and therefore cannot solve these issues. Hence we propose to take advantage of the image content stability provided by each single-view video to recover any surface regions visible by at least one camera. In particular we present an original probabilistic framework to derive and predict the true surface of models. We propose to fuse multi-view structure-from-motion with robust 3D features obtained by MVS in order to significantly improve reconstruction completeness and accuracy. A min-cut problem where all exact features serve as priors is solved in a final step to reconstruct the 3D models. In addition, experimental results were conducted on synthetic and challenging real world datasets to illustrate the robustness and accuracy of our method.