Dehazing cost volume for deep multi-view stereo in scattering media with airlight and scattering coefficient estimation
Dehazing cost volume for deep multi-view stereo in scattering media with airlight and scattering coefficient estimation
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DOI：
10.1016/j.cviu.2021.103253
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发表时间：
2021-08-11
影响因子：
4.5
通讯作者：
Iiyama, Masaaki
中科院分区：
文献类型：
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作者：
Fujimura, Yuki;Sonogashira, Motoharu;Iiyama, Masaaki
我们提出了一种基于学习的多视角立体（MVS）方法，用于雾或烟等散射介质，并提出了一种新的成本体积，称为除雾成本体积。在散射介质中捕获的图像由于光散射和悬浮粒子引起的衰减而退化。这种退化取决于场景深度；因此，传统的MVS方法难以评估光度一致性，因为在三维（3D）重建之前深度未知。消雾成本体通过计算成本体中扫描平面的散射效果，解决了深度估计和图像恢复的鸡和蛋问题。我们还提出了一种估算除雾成本所需的散射参数（如空气光）和散射系数的方法。网络的输出深度与我们的除雾成本体积可以看作是这些参数的函数；因此，它们是几何优化与稀疏的三维点云得到的结构从运动步骤。在合成雾霾图像上的实验结果表明，在散射介质条件下，我们的消雾成本体积比普通消雾成本体积更有效。我们还演示了我们的除雾成本量对真实雾场景的适用性。
We propose a learning-based multi-view stereo (MVS) method in scattering media, such as fog or smoke, with a novel cost volume, called the dehazing cost volume. Images captured in scattering media are degraded due to light scattering and attenuation caused by suspended particles. This degradation depends on scene depth; thus, it is difficult for traditional MVS methods to evaluate photometric consistency because the depth is unknown before three-dimensional (3D) reconstruction. The dehazing cost volume can solve this chicken-and egg problem of depth estimation and image restoration by computing the scattering effect using swept planes in the cost volume. We also propose a method of estimating scattering parameters, such as airlight, and a scattering coefficient, which are required for our dehazing cost volume. The output depth of a network with our dehazing cost volume can be regarded as a function of these parameters; thus, they are geometrically optimized with a sparse 3D point cloud obtained at a structure-from-motion step. Experimental results on synthesized hazy images indicate the effectiveness of our dehazing cost volume against the ordinary cost volume regarding scattering media. We also demonstrated the applicability of our dehazing cost volume to real foggy scenes.