In recent years, the research community has shown a lot of interest to
panoramic images that offer a 360-degree directional perspective. Multiple data
modalities can be fed, and complimentary characteristics can be utilized for
more robust and rich scene interpretation based on semantic segmentation, to
fully realize the potential. Existing research, however, mostly concentrated on
pinhole RGB-X semantic segmentation. In this study, we propose a
transformer-based cross-modal fusion architecture to bridge the gap between
multi-modal fusion and omnidirectional scene perception. We employ
distortion-aware modules to address extreme object deformations and panorama
distortions that result from equirectangular representation. Additionally, we
conduct cross-modal interactions for feature rectification and information
exchange before merging the features in order to communicate long-range
contexts for bi-modal and tri-modal feature streams. In thorough tests using
combinations of four different modality types in three indoor panoramic-view
datasets, our technique achieved state-of-the-art mIoU performance: 60.60% on
Stanford2D3DS (RGB-HHA), 71.97% Structured3D (RGB-D-N), and 35.92% Matterport3D
(RGB-D). We plan to release all codes and trained models soon.

本研究提出了一种基于 Transformer 的跨模态融合架构，用于弥合多模态融合与全景场景感知之间的差距，并使用失真感知模块来处理极端对象变形和全景失真，通过跨模态交互实现特征矫正和信息交换，最终将特征合并以传达双模态和三模态特征流的长程上下文，通过在三个室内全景数据集中四种不同模态类型的组合进行彻底测试，我们的技术在 Stanford2D3DS（RGB-HHA）上达到了 60.60% 的 mIoU 性能，Structured3D（RGB-D-N）上达到了 71.97% 的 mIoU 性能，Matterport3D（RGB-D）上达到了 35.92% 的 mIoU 性能。