The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation
Abstract
In multi-class indoor semantic segmentation using RGB-D data, it has been shown that incorporating depth feature into RGB feature is helpful to improve segmentation accuracy. However, previous studies have not fully exploited the potentials of multi-modal feature fusion, e.g., simply concatenating RGB and depth features or averaging RGB and depth score maps. To learn the optimal fusion of multimodal features, this paper presents a novel network that extends the core idea of residual learning to RGB-D semantic segmentation. Our network effectively captures multilevel RGB-D CNN features by including multi-modal feature fusion blocks and multi-level feature refinement blocks. Feature fusion blocks learn residual RGB and depth features and their combinations to fully exploit the complementary characteristics of RGB and depth data. Feature refinement blocks learn the combination of fused features from multiple levels to enable high-resolution prediction. Our network can efficiently train discriminative multi-level features from each modality end-to-end by taking full advantage of skip-connections. Our comprehensive experiments demonstrate that the proposed architecture achieves the state-of-the-art accuracy on two challenging RGB-D indoor datasets, NYUDv2 and SUN RGB-D.
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Cites
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Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
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He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
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Cited by
Image segmentation using deep learning: A survey
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300537 · Cited by 731
Chen, X., Lin, K.-Y., Wang, J., Wu, W., Qian, C., Li, H., & Zeng, G. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_33 · Cited by 488
GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation
Zhou, W., Liu, J., Lei, J., Yu, L., & Hwang, J.-N. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3109518 · Cited by 344
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection
Chen, H., & Li, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00322 · Cited by 336
Metadata
- Authors
- Lee, S., Park, S.-J., & Hong, K.-S.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 4990-4999
- OpenAlex
- W2775906317
- Cited by
- 285
- References
- 17
- FWCI
- 5.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lee, S., Park, S.-J., & Hong, K.-S. (2017). RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.533
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