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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
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Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assisted) person perception, (semantic) free space detection, (semantic) mapping, and (semantic) navigation. In this paper, we propose an efficient and robust RGB-D segmentation approach that can be optimized to a high degree using NVIDIA TensorRT and, thus, is well suited as a common initial processing step in a complex system for scene analysis on mobile robots. We show that RGB-D segmentation is superior to processing RGB images solely and that it can still be performed in real time if the network architecture is carefully designed. We evaluate our proposed Efficient Scene Analysis Network (ESANet) on the common indoor datasets NYUv2 and SUNRGB-D and show that we reach state-of-the-art performance while enabling faster inference. Furthermore, our evaluation on the outdoor dataset Cityscapes shows that our approach is suitable for other areas of application as well. Finally, instead of presenting benchmark results only, we also show qualitative results in one of our indoor application scenarios.
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“| ESANet [102] | ResNet34 [16] | 80.09 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [102]
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Kawai, H., & KURODA, Y. (2023) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii55687.2023.10039292 · Cited by 3
EFINet: Efficient Feature Interaction Network for Real-Time RGB-D Semantic Segmentation
Yang, Z., Mu, B., Wang, M., Wang, X., Xu, J., Yang, B., Yang, C., Li, H., & Lv, R. (2024) · IEEE Access · doi:10.1109/access.2024.3478746 · Cited by 3
Difference-Aware Fusion Network for Efficient RGB-D Semantic Segmentation in Indoor Robots
Yang, Y., Hong, Y., Yuan, Y., Pan, H., & Sun, W. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3575132 · Cited by 3
Yi, X., Zhang, H., Wang, Y., Guo, S., Wu, J., & Fan, C. (2022) · Sensors · doi:10.3390/s22166229 · Cited by 3
A Survey on the Deployability of Semantic Segmentation Networks for Fluvial Navigation
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Depth and Pixel-Distance Based Attention for Outdoor Semantic Segmentation
Woo, M.-W., & Seo, S. (2023) · IEEE Access · doi:10.1109/access.2023.3238414 · Cited by 3
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Metadata
- Authors
- Seichter, D., Kohler, M., Lewandowski, B., Wengefeld, T., & Gross, H.-M.
- Affiliations
- Technische Universität Ilmenau (DE)
- Technische Universität Ilmenau,Neuroinformatics and Cognitive Robotics Lab,Ilmenau,Germany,98693
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 13525-13531
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3100969133
- Cited by
- 330
- References
- 49
- Citations by year
- 2026: 36 · 2025: 76 · 2024: 92 · 2023: 70 · 2022: 40 · 2021: 16
- FWCI
- 17.83
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Education
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- RGB-D semantic segmentation, mobile robots, real-time inference, semantic mapping, semantic navigation, person perception, NYUv2, cityscape
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