To estimate the volume density and color of a 3D point in the multi-view image-based rendering, a common approach is to inspect the consensus existence among the given source image features, which is one of the informative cues for the estimation procedure. To this end, most of the previous methods utilize equally-weighted aggregation features. However, this could make it hard to check the consensus existence when some outliers, which frequently occur by occlusions, are included in the source image feature set. In this paper, we propose a novel source-view-wise feature aggregation method, which facilitates us to find out the consensus in a robust way by leveraging local structures in the feature set. We first calculate the source-view-wise distance distribution for each source feature for the proposed aggregation. After that, the distance distribution is converted to several similarity distributions with the proposed learnable similarity mapping functions. Finally, for each element in the feature set, the aggregation features are extracted by calculating the weighted means and variances, where the weights are derived from the similarity distributions. In experiments, we validate the proposed method on various benchmark datasets, including synthetic and real image scenes. The experimental results demonstrate that incorporating the proposed features improves the performance by a large margin, resulting in the state-of-the-art performance.
翻译:为了估计多视图图像制成中的3D点的体积密度和颜色,一个共同的方法是检查特定源图像特征之间的共识存在情况,这是估算程序的信息提示之一。为此,大多数先前的方法都使用同等加权汇总特征。然而,如果在源图像特征集中包括了经常因隐蔽而出现的一些外星,这就难以检查共识存在情况。在本文中,我们提出了一个新颖的源视图特征汇总方法,通过在设定的特性中利用本地结构,帮助我们以稳健的方式找到共识。我们首先计算拟议汇总的每种源特征的源视图远程分布。之后,将距离分布转换成若干相似分布,并使用拟议的可学习相似映射功能。最后,对于设定的每个要素,通过计算加权手段和差异(其重量来自相似分布)来提取汇总特征。在实验中,我们验证了各种基准数据集的拟议方法,包括合成和真实图像的大小比例。随后的性能实验结果将显示成像的性能。