The raw depth image captured by indoor depth sensors usually has an extensive
range of missing depth values due to inherent limitations such as the inability
to perceive transparent objects and the limited distance range. The incomplete
depth map with missing values burdens many downstream vision tasks, and a
rising number of depth completion methods have been proposed to alleviate this
issue. While most existing methods can generate accurate dense depth maps from
sparse and uniformly sampled depth maps, they are not suitable for
complementing large contiguous regions of missing depth values, which is common
and critical in images captured in indoor environments. To overcome these
challenges, we design a novel two-branch end-to-end fusion network named
RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to
predict a dense and completed depth map. The first branch employs an
encoder-decoder structure, by adhering to the Manhattan world assumption and
utilizing normal maps from RGB-D information as guidance, to regress the local
dense depth values from the raw depth map. In the other branch, we propose an
RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained
textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate
the features across the two branches, and we append a confidence fusion head to
fuse the two outputs of the branches for the final depth map. Extensive
experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method
clearly improves the depth completion performance, especially in a more
realistic setting of indoor environments, with the help of our proposed pseudo
depth maps in training.

使用 RDFC-GAN 模型基于 Manhattan world 假设将 RGB-D 信息转换为具有高纹理的深度图，并通过自适应融合模块 W-AdaIN 和 confidence fusion head 来解决室内深度图中大量连续缺失数据的深度完成问题。

室内深度填充的 RGB - 深度融合 CycleGAN (RDFC-GAN)

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

We propose a novel visual-inertial odometry approach that adopts structural
regularity in man-made environments. Instead of using Manhattan world
assumption, we use Atlanta world model to describe such regularity. An Atlanta
world is a world that contains multiple local Manhattan worlds with different
heading directions. Each local Manhattan world is detected on-the-fly, and
their headings are gradually refined by the state estimator when new
observations are coming. With fully exploration of structural lines that
aligned with each local Manhattan worlds, our visual-inertial odometry method
become more accurate and robust, as well as much more flexible to different
kinds of complex man-made environments. Through extensive benchmark tests and
real-world tests, the results show that the proposed approach outperforms
existing visual-inertial systems in large-scale man-made environments

提出了一种新的视觉惯性测距方法，采用 Atlanta World 模型，而不是 Manhattan World 假设，探索了与每个本地 Manhattan World 对齐的结构线，提高了测距方法的准确性和鲁棒性，并在大规模人造环境中表现出相对于现有视觉惯性系统更好的性能。