Accurately estimating depth in 360-degree imagery is crucial for virtual
reality, autonomous navigation, and immersive media applications. Existing
depth estimation methods designed for perspective-view imagery fail when
applied to 360-degree images due to different camera projections and
distortions, whereas 360-degree methods perform inferior due to the lack of
labeled data pairs. We propose a new depth estimation framework that utilizes
unlabeled 360-degree data effectively. Our approach uses state-of-the-art
perspective depth estimation models as teacher models to generate pseudo labels
through a six-face cube projection technique, enabling efficient labeling of
depth in 360-degree images. This method leverages the increasing availability
of large datasets. Our approach includes two main stages: offline mask
generation for invalid regions and an online semi-supervised joint training
regime. We tested our approach on benchmark datasets such as Matterport3D and
Stanford2D3D, showing significant improvements in depth estimation accuracy,
particularly in zero-shot scenarios. Our proposed training pipeline can enhance
any 360 monocular depth estimator and demonstrates effective knowledge transfer
across different camera projections and data types. See our project page for
results: this https URL

360 度图像的深度估计是虚拟现实、自主导航和沉浸式媒体应用中至关重要的，本研究提出了一种使用未标记 360 度数据的深度估计框架，通过六面立方体投影技术生成伪标签来高效标注 360 度图像的深度，并在深度估计的准确度上展示了显著改进，特别是在零样本情况下。

深度无处不在：通过透视蒸馏和无标签数据增强提升 360 度单目深度估计

Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective  Distillation and Unlabeled Data Augmentation

Ideally, 360{\deg} imagery could inherit the deep convolutional neural
networks (CNNs) already trained with great success on perspective projection
images. However, existing methods to transfer CNNs from perspective to
spherical images introduce significant computational costs and/or degradations
in accuracy. In this work, we present the Kernel Transformer Network (KTN).
KTNs efficiently transfer convolution kernels from perspective images to the
equirectangular projection of 360{\deg} images. Given a source CNN for
perspective images as input, the KTN produces a function parameterized by a
polar angle and kernel as output. Given a novel 360{\deg} image, that function
in turn can compute convolutions for arbitrary layers and kernels as would the
source CNN on the corresponding tangent plane projections. Distinct from all
existing methods, KTNs allow model transfer: the same model can be applied to
different source CNNs with the same base architecture. This enables application
to multiple recognition tasks without re-training the KTN. Validating our
approach with multiple source CNNs and datasets, we show that KTNs improve the
state of the art for spherical convolution. KTNs successfully preserve the
source CNN's accuracy, while offering transferability, scalability to typical
image resolutions, and, in many cases, a substantially lower memory footprint.

本文介绍了核变换网络，它可以有效地将卷积核从透视图图像转换为 360 度图像的等矩形投影。与现有方法不同，它允许模型转移，并成功地实现了在识别任务上的应用，同时提高了球面卷积的性能。