Triple fusion and feature pyramid decoder for RGB-D semantic segmentation

Abstract

Current RGB-D semantic segmentation networks incorporate depth information as an extra modality and merge RGB and depth features using methods such as equal-weighted concatenation or simple fusion strategies. However, these methods hinder the effective utilization of cross-modal information. Aiming at the problem that existing RGB-D semantic segmentation networks fail to fully utilize RGB and depth features, we propose an RGB-D semantic segmentation network, based on triple fusion and feature pyramid decoding, which achieves bidirectional interaction and fusion of RGB and depth features via the proposed three-stage cross-modal fusion module (TCFM). The TCFM proposes utilizing cross-modal cross-attention to intermix the data from two modalities into another modality. It fuses the RGB attributes and depth features proficiently, utilizing the channel-adaptive weighted fusion module. Furthermore, this paper introduces a lightweight feature pyramidal decoder network to fuse the multi-scale parts taken out by the encoder effectively. Experiments on NYU Depth V2 and SUN RGB-D datasets demonstrate that the cross-modal feature fusion network proposed in this study efficiently segments intricate scenes.