基于信念传播的多视图深度图恢复

Tao Li, Xiangyang Ji, Qionghai Dai
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引用次数: 0

摘要

深度图在多视图系统中起着重要的作用。对于多视图,深度图不仅可以用于基于图像的绘制(IBR),还可以通过将深度图合并到3D模型中进行多视图立体重建。本文提出了一种新的多视角深度图恢复方法。我们的工作最大的贡献是将鲁棒的光一致性度量与信念传播算法相结合,产生了几个优点。首先,通过鲁棒的光一致性度量,我们的方法有效地利用了来自多个视图的深度信息,并解决了遮挡问题。其次,通过考虑深度不连续的信念传播方法,可以有效地计算出无纹理区域中像素的深度值;最后,该方法可以处理各种场景。实验结果表明,该方法可以恢复令人满意的深度图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depth map recovery for multi-view using belief propagation
Depth maps are playing an important role in the multi-view system. For multi-view, depth maps can be used not only for image-based rendering (IBR), but also for multi-view stereo reconstruction by merging depth maps into 3D models. In this paper, we propose a novel approach to recover depth maps for multi-view. The most contribution of our work is the combination of a robust photo-consistency metric and the belief propagation algorithm, which results in several advantages. First, with the robust photo-consistency metric, our approach effectively utilizes the depth information from the multiple views and solves the occlusion problem as well. Second, the depth values of pixels in textureless regions can be effectively computed by belief propagation with depth discontinuity concerned. Finally, the proposed approach can deal with various scenes. The experimental results show that our approach can recover satisfactory depth maps.
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