Over the years, the use of superpixel segmentation has become very popular in various applications, serving as a preprocessing step to reduce data size by adapting to the content of the image, regardless of its semantic content. While the superpixel segmentation of standard planar images, captured with a 90{\deg} field of view, has been extensively studied, there has been limited focus on dedicated methods to omnidirectional or spherical images, captured with a 360{\deg} field of view. In this study, we introduce the first deep learning-based superpixel segmentation approach tailored for omnidirectional images called DSS (for Deep Spherical Superpixels). Our methodology leverages on spherical CNN architectures and the differentiable K-means clustering paradigm for superpixels, to generate superpixels that follow the spherical geometry. Additionally, we propose to use data augmentation techniques specifically designed for 360{\deg} images, enabling our model to efficiently learn from a limited set of annotated omnidirectional data. Our extensive validation across two datasets demonstrates that taking into account the inherent circular geometry of such images into our framework improves the segmentation performance over traditional and deep learning-based superpixel methods. Our code is available online.

翻译：多年来，超像素分割作为预处理步骤，通过适应图像内容（无论其语义内容如何）来减少数据量，已在各种应用中变得非常流行。虽然针对标准平面图像（采用90°视场捕获）的超像素分割已得到广泛研究，但专门针对全向或球形图像（采用360°视场捕获）的方法关注有限。在本研究中，我们提出了首个专为全向图像设计的深度学习超像素分割方法，称为DSS（深度球形超像素）。我们的方法利用球形CNN架构和可微分K均值聚类范式生成遵循球形几何的超像素。此外，我们提出使用专门为360°图像设计的数据增强技术，使我们的模型能够从有限的标注全向数据中高效学习。我们在两个数据集上的广泛验证表明，将此类图像固有的圆形几何特性纳入我们的框架，相比传统和基于深度学习的超像素方法，提高了分割性能。我们的代码已在线公开。