  Understanding indoor scenes is crucial for urban studies. Considering the
dynamic nature of indoor environments, effective semantic segmentation requires
both real-time operation and high accuracy.To address this, we propose
AsymFormer, a novel network that improves real-time semantic segmentation
accuracy using RGB-D multi-modal information without substantially increasing
network complexity. AsymFormer uses an asymmetrical backbone for multimodal
feature extraction, reducing redundant parameters by optimizing computational
resource distribution. To fuse asymmetric multimodal features, a Local
Attention-Guided Feature Selection (LAFS) module is used to selectively fuse
features from different modalities by leveraging their dependencies.
Subsequently, a Cross-Modal Attention-Guided Feature Correlation Embedding
(CMA) module is introduced to further extract cross-modal representations. The
AsymFormer demonstrates competitive results with 54.1% mIoU on NYUv2 and 49.1%
mIoU on SUNRGBD. Notably, AsymFormer achieves an inference speed of 65 FPS (79
FPS after implementing mixed precision quantization) on RTX3090, demonstrating
that AsymFormer can strike a balance between high accuracy and efficiency.
