Depth completion aims to recover dense depth maps from sparse ones, where
color images are often used to facilitate this task. Recent depth methods
primarily focus on image guided learning frameworks. However, blurry guidance
in the image and unclear structure in the depth still impede their performance.
To tackle these challenges, we explore an efficient repetitive design in our
image guided network to gradually and sufficiently recover depth values.
Specifically, the efficient repetition is embodied in both the image guidance
branch and depth generation branch. In the former branch, we design a dense
repetitive hourglass network to extract discriminative image features of
complex environments, which can provide powerful contextual instruction for
depth prediction. In the latter branch, we introduce a repetitive guidance
module based on dynamic convolution, in which an efficient convolution
factorization is proposed to reduce the complexity while modeling
high-frequency structures progressively. Extensive experiments indicate that
our approach achieves superior or competitive results on KITTI, VKITTI, NYUv2,
3D60, and Matterport3D datasets.

通过引入高效的重复设计和卷积分解技术，我们提出了一种图像引导的网络方法，有效地从稀疏图像中逐步恢复出密集的深度图，实验结果在多个数据集上都达到了优秀的效果。

RigNet++：高效的重复图像引导深度完成网络

RigNet++: Efficient Repetitive Image Guided Network for Depth Completion

Depth completion deals with the problem of recovering dense depth maps from
sparse ones, where color images are often used to facilitate this task. Recent
approaches mainly focus on image guided learning frameworks to predict dense
depth. However, blurry guidance in the image and unclear structure in the depth
still impede the performance of the image guided frameworks. To tackle these
problems, we explore a repetitive design in our image guided network to
gradually and sufficiently recover depth values. Specifically, the repetition
is embodied in both the image guidance branch and depth generation branch. In
the former branch, we design a repetitive hourglass network to extract
discriminative image features of complex environments, which can provide
powerful contextual instruction for depth prediction. In the latter branch, we
introduce a repetitive guidance module based on dynamic convolution, in which
an efficient convolution factorization is proposed to simultaneously reduce its
complexity and progressively model high-frequency structures. Extensive
experiments show that our method achieves superior or competitive results on
KITTI benchmark and NYUv2 dataset.

本研究提出了一种基于图像引导的神经网络，在处理深度不完整的情况下，通过引入重复设计和小时 glass 网络和动态卷积技术，提高了深度预测精度，在 KITTI 基准测试和 NYUv2 数据集上均取得了优越或有竞争力的结果。