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.

我们在雾或烟等散射介质中提出了一种基于学习的多视图立体 (MVS) 方法，其具有新的成本量，称为去雾成本量。由于悬浮颗粒引起的光散射和衰减，在散射介质中捕获的图像质量下降。这种退化取决于场景深度；因此，传统的 MVS 方法很难评估光度一致性，因为在三维 (3D) 重建之前深度是未知的。去雾代价量可以通过使用代价量中的扫掠平面计算散射效应来解决深度估计和图像恢复的鸡与蛋问题。我们还提出了一种估计散射参数的方法，例如空气光和散射系数，这是我们的去雾成本量所需的。具有我们的去雾成本量的网络的输出深度可以被视为这些参数的函数；因此，它们使用在结构-运动步骤中获得的稀疏 3D 点云进行几何优化。合成模糊图像的实验结果表明，我们的去雾成本量相对于散射介质的普通成本量的有效性。我们还展示了我们的去雾成本量对真实有雾场景的适用性。