SphereUFormer：用于球面360感知的U形Transformer
SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception
AI总结：
针对等矩形投影畸变及球面卷积方法性能不足的问题，提出SphereUFormer，以球面局部自注意力等模块直接在球面域建模，在深度估计与语义分割基准上达到最优。
摘要：
本文提出了一种用于全向360°感知的新颖方法。以往大多数常见方法依赖等矩形投影。这种表示很容易适用于二维操作层，但会给图像引入畸变。其他方法试图通过保持球面表示来消除畸变，但依赖复杂的卷积核，未能展现出具有竞争力的结果。在本工作中，我们引入了一种基于Transformer的架构，通过结合新颖的“球面局部自注意力”和其他面向球面的模块，成功在球面域中运行，并在深度估计和语义分割的360°感知基准测试中优于最先进方法。
英文摘要：
This paper proposes a novel method for omnidirectional 360$\degree$ perception. Most common previous methods relied on equirectangular projection. This representation is easily applicable to 2D operation layers but introduces distortions into the image. Other methods attempted to remove the distortions by maintaining a sphere representation but relied on complicated convolution kernels that failed to show competitive results. In this work, we introduce a transformer-based architecture that, by incorporating a novel ``Spherical Local Self-Attention'' and other spherically-oriented modules, successfully operates in the spherical domain and outperforms the state-of-the-art in 360$\degree$ perception benchmarks for depth estimation and semantic segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
