  Efficient RGB-D semantic segmentation has received considerable attention in
mobile robots, which plays a vital role in analyzing and recognizing
environmental information. According to previous studies, depth information can
provide corresponding geometric relationships for objects and scenes, but
actual depth data usually exist as noise. To avoid unfavorable effects on
segmentation accuracy and computation, it is necessary to design an efficient
framework to leverage cross-modal correlations and complementary cues. In this
paper, we propose an efficient lightweight encoder-decoder network that reduces
the computational parameters and guarantees the robustness of the algorithm.
Working with channel and spatial fusion attention modules, our network
effectively captures multi-level RGB-D features. A globally guided local
affinity context module is proposed to obtain sufficient high-level context
information. The decoder utilizes a lightweight residual unit that combines
short- and long-distance information with a few redundant computations.
Experimental results on NYUv2, SUN RGB-D, and Cityscapes datasets show that our
method achieves a better trade-off among segmentation accuracy, inference time,
and parameters than the state-of-the-art methods. The source code will be at
https://github.com/MVME-HBUT/SGACNet
