Cited by 330Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Vision and Imaging

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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

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Seichter, Daniel, Kohler, Mona, Lewandowski, Benjamin, Wengefeld, Tim, Gross, Horst-Michael·2021·2021 IEEE International Conference on Robotics and Automation (ICRA)

DOI 10.1109/icra48506.2021.9561675

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Abstract

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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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
Pyramid Scene Parsing Network
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
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Liu, J., Zhou, W., Fang, M., Mao, S., & Yang, R. (2024) · Infrared Physics & Technology · doi:10.1016/j.infrared.2024.105167 · Cited by 3
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Wu, D., Ma, Z., Xu, W., He, H., & Li, Z. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15030123 · Cited by 3
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Sang, H., Jiang, R., Li, X., Wang, Z., Zhou, Y., & He, B. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3401684 · Cited by 3
Online Scene Semantic Understanding Based on Sparsely Correlated Network for AR
Wang, Q., Song, J., Du, C., & Wang, C. (2024) · Sensors · doi:10.3390/s24144756 · Cited by 3
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Ma, J., Zhou, W., Lei, J., Yu, L., & Luo, T. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01417-6 · Cited by 3
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Wu, F. 中., Liu, S., Zhu, K. 中., Li, X., Guo, B., Yu, Z., Wen, H., Xu, X. P., Wang, L., & Liu, X. 中. (2024) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3666025.3699361 · Cited by 3
GANet: geometry-aware network for RGB-D semantic segmentation
Tian, C., Wei-rong, X., Bai, L., Yang, J., & Xu, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06337-0 · Cited by 3
Multimodal Deep Learning with Boosted Trees for Edge Inference
Chong, P., Wynter, L., & Chaudhury, B. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00021 · Cited by 2
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Foria, F., di Meglio, M., Calicchio, M., Brichese, M., Panico, F., & Miceli, G. (2024) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine62247.2024.10797001 · Cited by 2
SYN-PBOX: A large-scale benchmark dataset of box-shaped objects for scene understanding of bin picking
Cui, J., Shi, J., Liu, C., Bai, S., Shu, X., Zuo, X., Qian, Q., Xu, D., & Deng, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2024.129215 · Cited by 2
SFDFNet: Leveraging spatial-frequency deep fusion for RGB-T semantic segmentation
An, G., Geng, Y., Fang, S., & Guo, J. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105605 · Cited by 2
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Ma, X., Cui, X., Pun, M.-O., & Cheng, Z. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.110161 · Cited by 2
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Bui, L., Phung, S. L., Di, Y., Duong, S. T. M., & Bouzerdoum, A. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104793 · Cited by 2
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Petrović, M., Miljković, Z., & Jokić, A. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-28715-2_1 · Cited by 2
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Zhao, S., & Zhang, Q. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220451 · Cited by 3
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Zhang, Y., Chen, B., Li, Y., Wang, H., Tan, L., Wang, C., & Zhang, H. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad6897 · Cited by 2
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Zhou, W., Yang, P., Qiu, W., & Qiang, F. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3446549 · Cited by 2
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Leroy, P., Abisset‐Chavanne, E., Pommier, R., & Montemurro, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111516 · Cited by 2
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Xiao, Y., Meng, J., Qiang, F., Dong, X., & Zhou, W. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3603265 · Cited by 2
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Liu, H., Guo, L., Zhou, Z., & Zhang, H. (2022) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew56448.2022.9859353 · Cited by 2
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Gao, Y. J., Che, X., Liu, Q., Bie, M., & Xu, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13901-0 · Cited by 3
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Rached, I., Hajji, R., & Landes, T. (2024) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-031-43699-4_36 · Cited by 2
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Gao, Z., Gao, P., Yin, W., Liu, Y., & Qin, Z. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447100 · Cited by 2
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Arefeen, M. A., Li, Z., Uddin, M. Y. S., & Das, A. (2023) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3576842.3582372 · Cited by 1
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Wang, H., Chai, Y., & Tao, T. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-6937-7_27 · Cited by 1
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Qiu, J., Jiang, C., Zhang, P., & Wang, H. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801849 · Cited by 1
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Bae, H., Kang, D., Chang, M., Jeong, K. Y., & Song, B. C. (2025) · IEEE Access · doi:10.1109/access.2025.3552782 · Cited by 1
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Zhou, W., Li, Y., & Liu, Y. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3575945 · Cited by 1
LFSSMam: Efficient Aggregation of Multi-Spatial-Angular-Modal Information Using Selective SSM for Light Field Semantic Segmentation
Yan, W., Chen, H., Wu, Q., Zhang, X., Fang, Q., Hu, S., & Wang, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3588269 · Cited by 3
EACNet: Efficient Asymmetric Cross-Modal RGB-D Semantic Segmentation Network for Indoor Robotic Perception
Mao, Y., Chen, Y., Zhu, Q., & Zhang, J. (2025) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls66240.2025.11065774 · Cited by 1
RGB-D indoor scene parsing via wavelet sub-band guided transformer
Xie, W., Liu, H., & Li, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113675 · Cited by 1
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Zhuang, Z., Zheng, L., Li, W., Liu, R., Lu, P., & Cheng, H. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127719 · Cited by 1
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Liu, J., Liu, J., Wang, Y., Li, S., & Li, J. (2025) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew68306.2025.11152142 · Cited by 1
Mobile Robot Environment Perception: Adaptable Real-Time Neural Network Architecture for RGB-D Ground Segmentation*
Ziegler, P., Franke, J. E., & Reitelshöfer, S. (2025) · European Conference on Mobile Robots · doi:10.1109/ecmr65884.2025.11163160 · Cited by 1
Vision Foundation Model-Driven Multiscale Expert Tuning for Multimodal Remote Sensing Semantic Segmentation
Li, J., Wang, Z., Xu, N., You, Z., & Huang, D.-S. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3627904 · Cited by 1
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Nadgir, V., Marques, J. M. C., & Hauser, K. K. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247463 · Cited by 1
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Jia, C., Liu, H., Shi, F., Cheng, X., & Chen, S. C. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3662156 · Cited by 1
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Gong, Y., Lu, J., Gao, Y., Zhao, J., Zhang, X., & Rahardja, S. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3677060 · Cited by 1
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Zeng, J., Xu, J., Jia, X., Deng, B., Zhai, Y., Qin, C., Coscia, P., Genovese, A., & Tian, X. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116299 · Cited by 1
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Fervers, F., Breuer, T., Stachowiak, G., Bullinger, S., Bodensteiner, C., & Arens, M. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010841800003124 · Cited by 1
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Ruiz-Barroso, P., Castro, F. M., & Guil, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36616-1_37 · Cited by 1
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Yang, X., Ke, D., & Gong, X. (2023) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp58612.2023.10405240 · Cited by 1
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Li, T., Zhou, Q., Wu, D., Sun, M., & Hu, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19051-9 · Cited by 1
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Yang, L., & Wu, X. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230351 · Cited by 1
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Wang, J., & Shi, Y. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3658073 · Cited by 1
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Liu, D., & Feng, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651134 · Cited by 1
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Wu, A., & Fu, L. (2024) · Applied Sciences · doi:10.3390/app14188329 · Cited by 1
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Liu, K., Zheng, Y., Xie, J., Xie, Y., Wang, F., Ma, L., Dai, C., & Lu, T. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3510800 · Cited by 1
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Noh, D., Lee, B. H., Kim, H., Lee, S., Kim, H., Kim, J., Choi, J., & Baek, S.-M. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3562370 · Cited by 1
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Hu, S., Bonardi, F., Bouchafa, S., & Désiré Sidibé, A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956575 · Cited by 1
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Wu, D., Ma, Z., Xu, W., He, H., & Li, Z. (2023) · Preprints.org · doi:10.20944/preprints202307.1477.v1 · Cited by 1
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Wei, J., Song, S., Liu, Y., Liu, J. P., & Wang, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5903448 · Cited by 1
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Barbosa, F. M., & Osório, F. S. (2021) · Anais do Computer on the Beach · doi:10.14210/cotb.v12.p072-079
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Ghiță, A. Ș., Nan, M., Iancu, D.-T., & Florea, A. M. (2021) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs52396.2021.00029
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何, 杰. (2022) · Computer Science and Application · doi:10.12677/csa.2022.124078
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Lin, C.-H., Lin, P.-M., Chen, C.-W., & Chen, M.-F. (2022) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist55454.2022.9827733
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Foria, F., Calicchio, M., Miceli, G., Xie, A., Cuccato, D., & Allegro, A. (2022) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine54828.2022.9967536
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Helnawan, A., Attamimi, M., & Irfansyah, A. N. (2023) · Jurnal Teknik ITS · doi:10.12962/j23373539.v12i1.110848
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Ogrodowczyk, M., Kurczalska, J., Eichner, J., & Mickiewicz, A. (2023) · doi:10.21428/594757db.bb0044c1
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董, 景. (2023) · Journal of Electrical Engineering · doi:10.12677/jee.2023.114021
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Wu, S., Chen, S., Wang, Y., Liu, H., Tao, Z., Zhu, Y., Shen, T., & Wang, K. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10864568
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Ge, Y., Ge, Q., & Gao, Z. (2026) · doi:10.1145/3810280.3810287
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Jia, D., Song, H., Zhao, C., & Zhang, H. (2025) · doi:10.36227/techrxiv.174495621.18762297/v1
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Jia, J., Li, Q., Xu, C. X., Zhang, L., & Zhao, S. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023064
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Ma, X., Xun, Z., Mao, B., Xun, Y., & Guo, H. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978834
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Li, Y., Cong, R., Wang, S., Shen, J., & Sheng, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6596-9_15
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Yao, J., Dong, C., Li, X., & Jiao, Y. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10342-3
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Wu, Y., Chen, Q., Shi, J., Deng, H., & Bai, S. (2025) · Intelligent Service Robotics · doi:10.1007/s11370-025-00609-x
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Dai, Y., & Cui, Z. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097679
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Bai, J., Yang, J., Xiang, T., & Cai, S. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3602267
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Chen, Y., Rastogi, C., Zhou, Z., & Norris, W. R. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163920
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Cui, X., Ma, X., Pun, M.-O., & Cheng, Z. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-96146-5_18
PCBNet: positional crossing and broad features network for indoor scene semantic segmentation
Hou, H., Wenwen, Z., Zhang, Z., Sun, W., Yang, Y. Y., Liu, G., Haipeng, H., Tianyuan, L., & Li, X. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07982-5
DiffuSeg: Diffusion-Enhanced Cross-Modal Semantic Segmentation for RGB-D
Yang, J., & Mao, M. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755706
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Gao, C., Lin, Y., Wang, W., & Zhang, S. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754782
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Rong, G., Feng, Q., Cao, J., Feng, X., Wang, J., & Yan, L. (2025) · Cluster Computing · doi:10.1007/s10586-025-05822-y
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Song, J., Liu, Y., Xu, J., & Morooka, K. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4094-5_32
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Yue, W., Chang, K., Liu, X., Tan, K., & Chen, W. (2025) · Symmetry · doi:10.3390/sym17122070

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
ORCID: Seichter, Daniel
Gross, Horst-Michael
Venue: 2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher: IEEE
Published: 2021-05-30
Pages: 13525-13531
ISSN: 1050-4729 (ISSN-L)
DOI: 10.1109/icra48506.2021.9561675
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
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

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