# Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation

arXiv: https://arxiv.org/abs/2308.06024 · HTML: https://mlsanity.com/paper/2308.06024/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning](https://mlsanity.com/paper/2001.06902/) (2020)
- [ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation](https://mlsanity.com/paper/1905.10089/) (2019)
- [In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images](https://mlsanity.com/paper/1903.08469/) (2019)
- [Lite-HRNet: A Lightweight High-Resolution Network](https://mlsanity.com/paper/2104.06403/) (2021)
- [ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network](https://mlsanity.com/paper/1811.11431/) (2018)
- [Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis](https://mlsanity.com/paper/2011.06961/) (2020)
- [ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation](https://mlsanity.com/paper/2108.10528/) (2021)
- [Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation](https://mlsanity.com/paper/2004.04534/) (2020)
- [Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks](https://mlsanity.com/paper/1905.09646/) (2019)
- [Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments](https://mlsanity.com/paper/2207.04526/) (2022)
- [Learning Fully Dense Neural Networks for Image Semantic Segmentation](https://mlsanity.com/paper/1905.08929/) (2019)
- [LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image](https://mlsanity.com/paper/2110.15045/) (2021)

## Built on by (most cited first, 4 of 4)

- [LuSeg: Efficient Negative and Positive Obstacles Segmentation via Contrast-Driven Multi-Modal Feature Fusion on the Lunar](https://mlsanity.com/paper/2503.11409/) (2025)
- [CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes](https://mlsanity.com/paper/2407.01328/) (2024)
- [Optimizing rgb-d semantic segmentation through multi-modal interaction and pooling attention](https://mlsanity.com/paper/2311.11312/) (2023)
- [Efficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidance](https://mlsanity.com/paper/2603.07570/) (2026)

## Abstract (arXiv)

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