Panoramic distortion poses a significant challenge in 360 depth estimation,
particularly pronounced at the north and south poles. Existing methods either
adopt a bi-projection fusion strategy to remove distortions or model long-range
dependencies to capture global structures, which can result in either unclear
structure or insufficient local perception. In this paper, we propose a
spherical geometry transformer, named SGFormer, to address the above issues,
with an innovative step to integrate spherical geometric priors into vision
transformers. To this end, we retarget the transformer decoder to a spherical
prior decoder (termed SPDecoder), which endeavors to uphold the integrity of
spherical structures during decoding. Concretely, we leverage bipolar
re-projection, circular rotation, and curve local embedding to preserve the
spherical characteristics of equidistortion, continuity, and surface distance,
respectively. Furthermore, we present a query-based global conditional position
embedding to compensate for spatial structure at varying resolutions. It not
only boosts the global perception of spatial position but also sharpens the
depth structure across different patches. Finally, we conduct extensive
experiments on popular benchmarks, demonstrating our superiority over
state-of-the-art solutions.

本文提出了一种名为 SGFormer 的球面几何转换器，旨在解决 360 深度估计中的全景畸变问题，并通过将球面几何先验引入视觉转换器来增强其性能。在实验中，我们对流行的基准数据集进行了广泛的测试，证明了我们方法优于现有最先进的解决方案。