DASC-SPT: Towards Self-Supervised Panoramic Semantic Segmentation

Self-Supervised Semantic Segmentation aiming to leverage masses of unlabeled data for boosting semantic segmentation has been rapidly emerging as an active task in recent years. However existing self-supervised semantic segmentation approaches mainly focus on planar images leaving multiple distorted objects encountered in panoramic images unexplored due to the formidable challenge of handling heterogeneous degrees of distortions across different locations. In this paper we propose a novel Self-Supervised Panoramic Semantic Segmentation model termed DASC-SPT built upon the mainstream contrastive learning framework. Towards distortions in panoramic images we present two structures to better learn from distorted features by applying planar images. For the input images of self-supervision we design a Spherical Projection Transformation (SPT) strategy that involves randomly projecting planar images onto various locations of the sphere to introduce the distortions. For pixel-wise distorted features we construct a Deformation-aware Sampling Consistency (DASC) framework to further utilize the shared content and discrepancies caused by different distortions of paired views where the deformation-aware consistency can be quantified on pixel-wise features. Both of the two components facilitate the model to adapt to distortions and boost panoramic semantic segmentation. Extensive comprehensive experiments on three panoramic datasets demonstrate the effectiveness and superiority of DASC-SPT approach.