For technical details, please refer to:
SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation
For semantic segmentation in urban scene understanding, RGB cameras alone often fail to capture a clear holistic topology, especially in challenging lighting conditions. Thermal signal is an informative additional channel that can bring to light the contour and fine-grained texture of blurred regions in low-quality RGB image. Aiming at RGB-T (thermal) segmentation, existing methods either use simple passive channel/spatial-wise fusion for cross-modal interaction, or rely on heavy labeling of ambiguous boundaries for fine-grained supervision. We propose a Spatial-aware Demand-guided Recursive Meshing (SpiderMesh) framework that: 1) proactively compensates inadequate contextual semantics in optically-impaired regions via a demand-guided target masking algorithm; 2) refines multimodal semantic features with recursive meshing to improve pixel-level semantic analysis performance. We further introduce an asymmetric data augmentation technique M-CutOut, and enable semi-supervised learning to fully utilize RGB-T labels only sparsely available in practical use. Extensive experiments on MFNet and PST900 datasets demonstrate that SpiderMesh achieves new state-of-the-art performance on standard RGB-T segmentation benchmarks.
This code has been tested with Python 3.8.10, Pytorch 1.11.0, CUDA 11.7.
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Setup environment
conda create -n SpiderMesh python=3.8.10 source activate SpiderMesh -
Clone the respository
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Install the requirements
pip install -r requirements.txt pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 -
Download dataset
Dataset Note Link MFNet preprocessed by RTFNet* Download:5ah9 PST900 original dataset Download The original link of the preprocessed MFNet (by RTFNet) seems not availuable now, we have upload it to our BaiduCloud.
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Download pretrained models
Dataset Description Link MFNet SpiderMesh-50 Download:btkl SpiderMesh-101 Download:5t2p SpiderMesh-152 Download:i3me Semi-supervised SpiderMesh-152 Download:8jif PST900 SpiderMesh-152 Download:5qzy
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Supervised learning
python train.py # for MFNet dataset python train_pst.py # for PST900 dataset
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Semi-supervised learning
python semi-train.py
For detailed analysis, three modes are supported: test(default), test_day, test_night.
python test.py # for MFNet dataset
python test_pst.py # for PST900 dataset- Complexity
If you find our work useful in your research, please consider citing:
@article{spidermesh,
title={{SpiderMesh}: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation},
author={S. Fan and Z. Wang and Y. Wang and J. Liu},
journal={arXiv:2303.08692},
year={2023}}