Volumetric phenomena, such as clouds and fog, present a significant challenge
for 3D reconstruction systems due to their translucent nature and their complex
interactions with light. Conventional techniques for reconstructing scattering
volumes rely on controlled setups, limiting practical applications. This paper
introduces an approach to reconstructing volumes from a few input stereo pairs.
We propose a novel deep learning framework that integrates a deep stereo model
with a 3D Convolutional Neural Network (3D CNN) and an advection module,
capable of capturing the shape and dynamics of volumes. The stereo depths are
used to carve empty space around volumes, providing the 3D CNN with a prior for
coping with the lack of input views. Refining our output, the advection module
leverages the temporal evolution of the medium, providing a mechanism to infer
motion and improve temporal consistency. The efficacy of our system is
demonstrated through its ability to estimate density and velocity fields of
large-scale volumes, in this case, clouds, from a sparse set of stereo image
pairs.

通过少量的立体图像对体积视图进行重建，该文介绍了一种新颖的深度学习框架，将深度立体模型与三维卷积神经网络（3D CNN）和迁移模块相结合，能够捕捉体积的形状和动态，从而估计出大规模体积（如云）的密度和速度场。

体积云场重建

Volumetric Cloud Field Reconstruction

Current self-supervised methods for monocular depth estimation are largely
based on deeply nested convolutional networks that leverage stereo image pairs
or monocular sequences during a training phase. However, they often exhibit
inaccurate results around occluded regions and depth boundaries. In this paper,
we present a simple yet effective approach for monocular depth estimation using
stereo image pairs. The study aims to propose a student-teacher strategy in
which a shallow student network is trained with the auxiliary information
obtained from a deeper and more accurate teacher network. Specifically, we
first train the stereo teacher network by fully utilizing the binocular
perception of 3-D geometry and then use the depth predictions of the teacher
network to train the student network for monocular depth inference. This
enables us to exploit all available depth data from massive unlabeled stereo
pairs. We propose a strategy that involves the use of a data ensemble to merge
the multiple depth predictions of the teacher network to improve the training
samples by collecting non-trivial knowledge beyond a single prediction. To
refine the inaccurate depth estimation that is used when training the student
network, we further propose stereo confidence-guided regression loss that
handles the unreliable pseudo depth values in occlusion, texture-less region,
and repetitive pattern. To complement the existing dataset comprising outdoor
driving scenes, we built a novel large-scale dataset consisting of one million
outdoor stereo images taken using hand-held stereo cameras. Finally, we
demonstrate that the monocular depth estimation network provides feature
representations that are suitable for high-level vision tasks. The experimental
results for various outdoor scenarios demonstrate the effectiveness and
flexibility of our approach, which outperforms state-of-the-art approaches.

使用基于学生 - 教师策略、数据集成和立体信心引导回归损失的方法，可以提高单目深度估计的精度，并用于高级视觉任务，如室外场景的驾驶情境等。

用于半监督单目深度估计的 RGB-D 大型数据集

A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation

Stereo image pairs can be used to improve the performance of super-resolution
(SR) since additional information is provided from a second viewpoint. However,
it is challenging to incorporate this information for SR since disparities
between stereo images vary significantly. In this paper, we propose a
parallax-attention stereo superresolution network (PASSRnet) to integrate the
information from a stereo image pair for SR. Specifically, we introduce a
parallax-attention mechanism with a global receptive field along the epipolar
line to handle different stereo images with large disparity variations. We also
propose a new and the largest dataset for stereo image SR (namely, Flickr1024).
Extensive experiments demonstrate that the parallax-attention mechanism can
capture correspondence between stereo images to improve SR performance with a
small computational and memory cost. Comparative results show that our PASSRnet
achieves the state-of-the-art performance on the Middlebury, KITTI 2012 and
KITTI 2015 datasets.

本文提出了一种基于视差注意力机制的立体图像超分辨率网络 (PASSRnet)，该机制通过包含沿极线的全局接受野处理具有大视差差异的不同立体图像，与 Flickr1024 数据集一起使用，通过小的计算和内存成本来提高超分辨率性能，并在 Middlebury、KITTI 2012 和 KITTI 2015 数据集上实现了最先进的性能。