Single Frame Semantic Segmentation Using Multi-Modal Spherical Images
Suresh Guttikonda Jason Rambach
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.