  Task-specific data-fusion networks have marked considerable achievements in
urban scene parsing. Among these networks, our recently proposed RoadFormer
successfully extracts heterogeneous features from RGB images and surface normal
maps and fuses these features through attention mechanisms, demonstrating
compelling efficacy in RGB-Normal road scene parsing. However, its performance
significantly deteriorates when handling other types/sources of data or
performing more universal, all-category scene parsing tasks. To overcome these
limitations, this study introduces RoadFormer+, an efficient, robust, and
adaptable model capable of effectively fusing RGB-X data, where ``X'',
represents additional types/modalities of data such as depth, thermal, surface
normal, and polarization. Specifically, we propose a novel hybrid feature
decoupling encoder to extract heterogeneous features and decouple them into
global and local components. These decoupled features are then fused through a
dual-branch multi-scale heterogeneous feature fusion block, which employs
parallel Transformer attentions and convolutional neural network modules to
merge multi-scale features across different scales and receptive fields. The
fused features are subsequently fed into a decoder to generate the final
semantic predictions. Notably, our proposed RoadFormer+ ranks first on the
KITTI Road benchmark and achieves state-of-the-art performance in mean
intersection over union on the Cityscapes, MFNet, FMB, and ZJU datasets.
Moreover, it reduces the number of learnable parameters by 65\% compared to
RoadFormer. Our source code will be publicly available at
mias.group/RoadFormerPlus.
