The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“| 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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Rethinking Atrous Convolution for Semantic Image Segmentation
Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1706.05587 · Cited by 7,517
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
Cited by
Sorted by citations
Items 301–330 of 330
Investigation of Audio Controlled Edge Semantic Segmentation System for Visually Impaired People
Wang, Y., Wang, Z., Hahn, N., Cheng, L., & Liu, S. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252696
Small Obstacle-Aware Person-Following for Mobile Robots Using Vision-Based Segmentation
Sim, J., Hagiwara, Y., & Choi, Y. (2025) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce65946.2025.11275154
Detail-Enhancement Dual-Domain Fusion Network for Optical-DSM Image Segmentation
Chen, Z., Xiang, W., Lin, Z., Yang, K., & Liu, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3644344
He, F., Wang, Z., Xu, N., You, Z., & Huang, D. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3648961 · Cited by 1
FEMINet: Real-Time RGB-D Semantic Segmentation via Feature Enhancement and Multi-level Interaction
Jia, L., Mao, Y., Lu, J. T., Pan, Z., Nie, B., & Wang, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5693-9_35
Zhang, C., Liu, P., Teng, J., & Liu, C. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341130
Verma, M. K., Pandey, A., Tripathi, Y., Singh, D., & Rehan, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15398-2_21
Outdoor Scene Dynamic Feature Point Filtering in SLAM Localization
Zhou, Y., Yang, Y., & Ye, L. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404630
Wang, W., Wang, D., Xu, C., & Jiang, Y. (2026) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/3798065.3798088
Wang, Y., Pan, Q., & Zhao, X. (2026) · International Conference on Artificial Intelligence and Control (ICAIC) · doi:10.1145/3807246.3807352
MultiMoE: Multimodal Mixture of Experts Adapter for Semantic Segmentation of Remote Sensing Images
Qi, Z., Guo, Q., Wang, L., & Zhou, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3702194
Multi‐Attention Guided Feature Collaborate Network for RGB‐D Semantic Segmentation
Song, J., Du, C., Wang, C., Li, H., Liu, R., & Qi, Y. (2026) · IET Image Processing · doi:10.1049/ipr2.70422
STELLA: a modular framework for SpatioTemporal Event-based Lagrangian particLe trAcking
Sachs, S., Jung, S., Kahl, M., Keuper, M., Willert, C. E., & Cierpka, C. (2026) · Experiments in Fluids · doi:10.1007/s00348-026-04253-8 · Cited by 1
Pan, X., Zhang, R., Xu, Z., & Zhao, T. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3714435
Lyu, B., Li, S. Y., Liu, C., Zhang, H., Hu, G., & Wang, X. (2026) · Remote Sensing · doi:10.3390/rs18152445
Wang, F., Liu, Q., & Wu, G. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08745-6
Zhou, W., Jian, B., Qiang, F., Yin, Z., & Yan, W. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3725155
Structure-Prior-Guided Multi-Stage Cross-Modal Collaborative Network for RGB-D Semantic Segmentation
Yu, Y., Zhong, Z., Min, F., & Deng, S. (2026) · Journal of Imaging · doi:10.3390/jimaging12080394
Multimodal Instance Segmentation of Building Façade Damage with Frequency-Decoupled Feature Fusion
Xu, S., Chen, Z., Wang, Y., Shi, Y., Lu, H., Gu, H., & Sun, Z. (2026) · Buildings · doi:10.3390/buildings16173396
Yee, A., Song, J., & McGuire, J. (2026) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase69719.2026.00097
Sun, Q., Hu, Z., Chen, L., Xiao, Z., Liu, S., Yang, H., & Chen, X. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3739418
He, C., Wang, Q., Yuan, B., & 谢闻悦 (2026) · GIScience & Remote Sensing · doi:10.1080/15481603.2026.2739002
Li, Y., Shi, L., Bao, X., Wang, Y., Li, Y., 蔡雪飞, & Li, F. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112453
Unbiased Dynamic Multimodal Fusion
Wei, S., Zhang, K., Chen, L., He, T., & Duan, G. (2026) · doi:10.1109/cvprf71909.2026.00599
Efficient Indoor Robotic Scene Analysis with Encoder-Centric Vision Transformers
Fischedick, S. B., Stephan, B., Seichter, D., & Groß, H. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729615
Self-citationVisual Object Removal in LiDAR Point Clouds for Dynamic Environment Representation
Mota, K. O. S., Garrote, L., Mendes, A., Premebida, C., & Nunes, U. (2026) · International Conference on Information Science and Technology · doi:10.1109/icist71072.2026.11727804
What Makes Multi-modal Learning Better than Single (Provably)
Huang, Y., Du, C., Xue, Z., Chen, X., Zhao, H., & Huang, L. (2021) · arXiv · doi:10.48550/arxiv.2106.04538 · Cited by 197
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
