Depth estimation is a fundamental issue in 4-D light field processing and
analysis. Although recent supervised learning-based light field depth
estimation methods have significantly improved the accuracy and efficiency of
traditional optimization-based ones, these methods rely on the training over
light field data with ground-truth depth maps which are challenging to obtain
or even unavailable for real-world light field data. Besides, due to the
inevitable gap (or domain difference) between real-world and synthetic data,
they may suffer from serious performance degradation when generalizing the
models trained with synthetic data to real-world data. By contrast, we propose
an unsupervised learning-based method, which does not require ground-truth
depth as supervision during training. Specifically, based on the basic
knowledge of the unique geometry structure of light field data, we present an
occlusion-aware strategy to improve the accuracy on occlusion areas, in which
we explore the angular coherence among subsets of the light field views to
estimate initial depth maps, and utilize a constrained unsupervised loss to
learn their corresponding reliability for final depth prediction. Additionally,
we adopt a multi-scale network with a weighted smoothness loss to handle the
textureless areas. Experimental results on synthetic data show that our method
can significantly shrink the performance gap between the previous unsupervised
method and supervised ones, and produce depth maps with comparable accuracy to
traditional methods with obviously reduced computational cost. Moreover,
experiments on real-world datasets show that our method can avoid the domain
shift problem presented in supervised methods, demonstrating the great
potential of our method.

提出一种基于无监督学习的深度估计方法，通过探索角度的一致性和权重平滑性，实现在真实场景数据下进行更高精度的深度估计。

基于遮挡感知的四维光场深度非监督学习

Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields

We present a new learning-based method for multi-frame depth estimation from
a color video, which is a fundamental problem in scene understanding, robot
navigation or handheld 3D reconstruction. While recent learning-based methods
estimate depth at high accuracy, 3D point clouds exported from their depth maps
often fail to preserve important geometric feature (e.g., corners, edges,
planes) of man-made scenes. Widely-used pixel-wise depth errors do not
specifically penalize inconsistency on these features. These inaccuracies are
particularly severe when subsequent depth reconstructions are accumulated in an
attempt to scan a full environment with man-made objects with this kind of
features. Our depth estimation algorithm therefore introduces a Combined Normal
Map (CNM) constraint, which is designed to better preserve high-curvature
features and global planar regions. In order to further improve the depth
estimation accuracy, we introduce a new occlusion-aware strategy that
aggregates initial depth predictions from multiple adjacent views into one
final depth map and one occlusion probability map for the current reference
view. Our method outperforms the state-of-the-art in terms of depth estimation
accuracy, and preserves essential geometric features of man-made indoor scenes
much better than other algorithms.

提出了一种基于学习的多帧深度估计方法，通过引入组合法线图约束和遮挡感知策略，能更好地保留室内场景中的高曲率特征和全局平面区域，并优于其他算法在深度估计精度和保存几何特征方面的表现。