We formalize concepts around geometric occlusion in 2D images (i.e., ignoring
semantics), and propose a novel unified formulation of both occlusion
boundaries and occlusion orientations via a pixel-pair occlusion relation. The
former provides a way to generate large-scale accurate occlusion datasets
while, based on the latter, we propose a novel method for task-independent
pixel-level occlusion relationship estimation from single images. Experiments
on a variety of datasets demonstrate that our method outperforms existing ones
on this task. To further illustrate the value of our formulation, we also
propose a new depth map refinement method that consistently improve the
performance of state-of-the-art monocular depth estimation methods. Our code
and data are available at this http URL

本文提出了一种基于像素对遮挡关系的新型统一表述方式，同时提供了一种生成大规模准确遮挡数据集的方法和一种基于该方式的单幅图像中的任务无关像素级遮挡关系估计新方法，并提出了一种新的深度图细化方法，从而在改善现有单目深度估计方法的性能方面表现出更好的效果。

像素对遮挡关系图（P2ORM）：构建、推理与应用

Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference &  Application

Deep Neural Networks (DNNs) have the potential to improve the quality of
image-based 3D reconstructions. However, the use of DNNs in the context of 3D
reconstruction from large and high-resolution image datasets is still an open
challenge, due to memory and computational constraints. We propose a pipeline
which takes advantage of DNNs to improve the quality of 3D reconstructions
while being able to handle large and high-resolution datasets. In particular,
we propose a confidence prediction network explicitly tailored for Multi-View
Stereo (MVS) and we use it for both depth map outlier filtering and depth map
refinement within our pipeline, in order to improve the quality of the final 3D
reconstructions. We train our confidence prediction network on (semi-)dense
ground truth depth maps from publicly available real world MVS datasets. With
extensive experiments on popular benchmarks, we show that our overall pipeline
can produce state-of-the-art 3D reconstructions, both qualitatively and
quantitatively.

本文提出了一个流程，利用深度神经网络提高三维重建的质量，同时能够处理大型和高分辨率的图像数据集，具体包括针对多视角立体视觉（Multi-View Stereo）设计的置信度预测网络，用于深度地图 outlier 过滤和深度地图细化，从而提高最终三维重建的质量。通过在流行的基准数据集上进行广泛的实验，我们展示了这个流程的优越性，产生了最先进的三维重建质量和效果。