360{\deg} cameras can capture complete environments in a single shot, which makes 360{\deg} imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360{\deg} data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose a flexible framework for monocular depth estimation from high-resolution 360{\deg} images using tangent images. We project the 360{\deg} input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360{\deg} depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://manurare.github.io/360monodepth/.

翻译：360=deg} 相机可以在一次性镜头中捕捉完整环境, 使360=deg}图像在许多计算机视觉任务中诱发360=deg}图像。 然而, 360=deg} 数据, 特别是对于2K( 2048x1024) 等高分辨率数据, 特别是对于新观点合成和虚拟现实应用非常重要的2K( 2048x1024) 等高分辨率数据, 特别是对于2K( 2048x1024) 及以上高分辨率数据, 这对于新观点合成和虚拟现实应用非常重要。 以CNN为基础的当前方法不支持这种高分辨率。 在这项工作中, 我们提出一个灵活的框架, 用于使用正切图像从高分辨率360=deg} 图像进行单方深度估计。 我们将360_ dedeg} 图像投放到一组产生视角视图的正方形平方形平面上, 这组图像适合最新、 最精确的艺术状态的单个深度估计。 为了实现全球一致的差异估计, 我们使用可变异度的多尺度校准的多尺度校正。 。 我们的源代码和数据可在 http:// bragiobs/ 360 。