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.
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ESANet, using a ResNet34 backbone, achieves an mIoU of 80.09 on the Cityscapes dataset for 2.5D semantic segmentation.
“| 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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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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_20 · Cited by 2,968 · see citing works
Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862 · see citing works
SUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943 · see citing works
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation
Romera, E., Álvarez, J. M., Bergasa, L. M., & Arroyo, R. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2750080 · Cited by 1,491 · see citing works
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902 · see citing works
ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network
Mehta, S., Rastegari, M., Shapiro, L., & Hajishirzi, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00941 · Cited by 551 · see citing works
ACNET: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
Hu, X., Yang, K., Fei, L., & Wang, K. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803025 · Cited by 500 · see citing works
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 · see citing works
Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
Wang, Y., Zhou, Q., Liu, J., Xiong, J., Gao, G., Wu, X., & Latecki, L. J. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803154 · Cited by 451 · see citing works
Oršić, M., Krešo, I., Bevandić, P., & Šegvić, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01289 · Cited by 437 · see citing works
Depth-Aware CNN for RGB-D Segmentation
Wang, W., & Neumann, U. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_9 · Cited by 314 · see citing works
RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation
Lee, S., Park, S., & Hong, K.-S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.533 · Cited by 294 · see citing works
Self-supervised model adaptation for multimodal semantic segmentation
Valada, A., Mohan, R., & Burgard, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01188-y · Cited by 293 · see citing works
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?
McCormac, J., Handa, A., Leutenegger, S., & Davison, A. J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.292 · Cited by 291 · see citing works
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic SegmentationPreprint
Li, G., Yun, I., Kim, J., & Kim, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11357 · Cited by 254 · see citing works
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Lo, S.-Y., Hang, H., Chan, S.-W., & Lin, J.-J. (2019) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3338533.3366558 · Cited by 248 · see citing works
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic SegmentationPreprint
Jiang, J., Zheng, L., Luo, F., & Zhang, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01054 · Cited by 233 · see citing works
Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation
Chen, L.-Z., Lin, Z., Wang, Z., Yang, Y., & Cheng, M.-M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049332 · Cited by 177 · see citing works
Gross, H.-M., Boehme, H.-J., Schröter, C., Mueller, S., Koenig, A., Einhorn, E., Martin, C., Merten, M., & Bley, A. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354497 · Cited by 163 · see citing works
Self-citationGroß, H., Mueller, S., Schröter, C., Volkhardt, M., Scheidig, A., Debes, K., Richter, K., & Doering, N. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354230 · Cited by 121 · see citing works
Hung, S.-W., Lo, S.-Y., & Hang, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803360 · Cited by 60 · see citing works
Groß, H., Scheidig, A., Müller, S., Schütz, B., Fricke, C., & Meyer, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793693 · Cited by 48 · see citing works
Malleable 2.5D Convolution: Learning Receptive Fields Along the Depth-Axis for RGB-D Scene Parsing
Xing, Y., Wang, J., & Zeng, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_33 · Cited by 45 · see citing works
Xiang, L., Echtler, F., Kerl, C., Wiedemeyer, T., L., Hanyazou, Gordon, R., Facioni, F., Laborer2008, Wareham, R., et al. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.594510 · Cited by 37 · see citing works
2.5D Convolution for RGB-D Semantic Segmentation
Xing, Y., Wang, J., Chen, X., & Zeng, G. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803757 · Cited by 35 · see citing works
Coupling Two-Stream RGB-D Semantic Segmentation Network by Idempotent Mappings
Xing, Y., Wang, J. B., Chen, X., & Zeng, G. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803146 · Cited by 31 · see citing works
Generic 2D/3D SLAM with NDT maps for lifelong application
Einhorn, E., & Groß, H. (2013) · European Conference on Mobile Robots · doi:10.1109/ecmr.2013.6698849 · Cited by 30 · see citing works
Xiang, L., Echtler, F., Kerl, C., et al. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.50641 · Cited by 29 · see citing works
3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation
Chen, Y., Mensink, T., & Gavves, E. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00028 · Cited by 29 · see citing works
Socially Compliant Human-Robot Interaction for Autonomous Scanning Tasks in Supermarket Environments
Lewandowski, B., Wengefeld, T., Müller, S., Jenny, M., Glende, S., Schröter, C., Bley, A., & Groß, H. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223568 · Cited by 29 · see citing works
Self-citationEfficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation
Lawin, F. J., Forssén, P.-E., & Ovrén, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_11 · Cited by 25 · see citing works
Groß, H., Meyer, S., Scheidig, A., Eisenbach, M., Mueller, S., Trinh, T. Q., Wengefeld, T., Bley, A., Martin, C., & Fricke, C. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989124 · Cited by 25 · see citing works
Self-citationMulti-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth ImagesPreprint
Fooladgar, F., & Shohreh Kasaei, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11691 · Cited by 25 · see citing works
3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes
Zhong, Y., Dai, Y., & Li, H. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545378 · Cited by 13 · see citing works
Small Data, Big Decisions: Model Selection in the Small-Data RegimePreprint
Bornschein, J., Visin, F., & Osindero, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12583 · Cited by 10 · see citing works
Multi-Task Deep Learning for Depth-based Person Perception in Mobile Robotics
Seichter, D., Lewandowski, B., Hochemer, D., Wengefeld, T., & Groß, H. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340870 · Cited by 10 · see citing works
Self-citationAutonomous Mobile Gait Training Robot for Orthopedic Rehabilitation in a Clinical Environment
Trinh, T. Q., Vorndran, A., Schuetz, B., Jaeschke, B., Mayfarth, A., Scheidig, A., & Groß, H. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223482 · Cited by 9 · see citing works
Flagged first, then by citations
2 of the 49 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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 · see citing works
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 · see citing works
A Multilevel Multimodal Fusion Transformer for Remote Sensing Semantic Segmentation
Ma, X., Zhang, X., Pun, M.-O., & Liu, M. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3373033 · Cited by 270 · see citing works
Delivering Arbitrary-Modal Semantic Segmentation
Zhang, J., Liu, R., Shi, H., Yang, K., Reiß, S., Peng, K., Fu, H., Wang, K., & Stiefelhagen, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00116 · Cited by 202 · see citing works
A Crossmodal Multiscale Fusion Network for Semantic Segmentation of Remote Sensing Data
Ma, X., Zhang, X., & Pun, M.-O. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3165005 · Cited by 184 · see citing works
UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes
Sheng, H., Cong, R., Yang, D., Chen, R., Wang, S., & Cui, Z. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3187664 · Cited by 178 · see citing works
PGDENet: Progressive Guided Fusion and Depth Enhancement Network for RGB-D Indoor Scene Parsing
Zhou, W., Yang, E., Lei, J., Wan, J., & Yu, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3161852 · Cited by 168 · see citing works
Xue, Z., & Marculescu, R. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00256 · Cited by 134 · see citing works
Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
Wan, Z., Zhang, P., Wang, Y., Yong, S., Stepputtis, S., Sycara, K. P., & Xie, Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00176 · Cited by 114 · see citing works
Zhou, W., Jianhui, J., Lei, J., & Yu, L. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3159032 · Cited by 94 · see citing works
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
Mittal, M., Hoeller, D., Farshidian, F., Hutter, M., & Garg, A. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981779 · Cited by 91 · see citing works
Edge Detection Guide Network for Semantic Segmentation of Remote-Sensing Images
Jianhui, J., Zhou, W., Yang, R., Ye, L., & Yu, L. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3234257 · Cited by 89 · see citing works
A Unified Framework With Multimodal Fine-Tuning for Remote Sensing Semantic Segmentation
Ma, X., Zhang, X., Pun, M.-O., & Huang, B. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3585238 · Cited by 78 · see citing works
Contrastive Multimodal Fusion with TupleInfoNCE
Liu, Y., Fan, Q., Zhang, S., Dong, H., Funkhouser, T. A., & Yi, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00079 · Cited by 77 · see citing works
Wei, S., Luo, C., & Luo, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01919 · Cited by 75 · see citing works
Glass Segmentation With RGB-Thermal Image Pairs
Huo, D., Wang, J., Qian, Y., & Yang, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3256762 · Cited by 73 · see citing works
Xiaomin, F., Zhou, W., Qian, X., & Yan, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121999 · Cited by 71 · see citing works
Jin, S., Wang, X., & Meng, Q. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111358 · Cited by 72 · see citing works
A brief survey on RGB-D semantic segmentation using deep learning
Wang, C., Wang, C., Li, W., & Wang, H. (2021) · Displays · doi:10.1016/j.displa.2021.102080 · Cited by 71 · see citing works
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
Yang, J., Bai, L., Sun, Y., Tian, C., Mao, M., & Wang, G. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3296162 · Cited by 65 · see citing works
Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image
Yin, W., Zhang, J., Wang, O., Niklaus, S., Chen, S., Liu, Y., & Shen, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3209968 · Cited by 60 · see citing works
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Seichter, D., Fischedick, S. B., Köhler, M., & Grob, H.-M. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892852 · Cited by 60 · see citing works
Self-citationRizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
Du, S., Wang, W., Guo, R., Wang, R., & Tang, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00756 · Cited by 59 · see citing works
Spatial Information-Guided Adaptive Context-Aware Network for Efficient RGB-D Semantic Segmentation
Zhang, Y., Xiong, C., Liu, J., Ye, X., & Sun, G. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3304637 · Cited by 53 · see citing works
DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation
Yin, B.-W., Cao, J.-L., Cheng, M., & Hou, Q. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01802 · Cited by 51 · see citing works
Transformer fusion for indoor RGB-D semantic segmentation
Wu, Z., Zhou, Z., Allibert, G., Stolz, C., Demonceaux, C., & Ma, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104174 · Cited by 50 · see citing works
Cross-modal attention fusion network for RGB-D semantic segmentation
Zhao, Q., Wan, Y., Xu, J., & Fang, L. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126389 · Cited by 49 · see citing works
Disentangled Cross-Modal Transformer for RGB-D Salient Object Detection and Beyond
Chen, H., Shen, F., Ding, D., Deng, Y., & Li, C. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3364022 · Cited by 49 · see citing works
CEGFNet: Common Extraction and Gate Fusion Network for Scene Parsing of Remote Sensing Images
Zhou, W., Jianhui, J., Lei, J., & Hwang, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3109626 · Cited by 48 · see citing works
Wang, Y., Yao, Y., Zhang, Y., Zhang, B., & Gao, Z. Y. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.06.014 · Cited by 45 · see citing works
DCANet: Differential convolution attention network for RGB-D semantic segmentation
Bai, L., Yang, J., Tian, C., Sun, Y., Mao, M., Xu, Y., & Wei-rong, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111379 · Cited by 44 · see citing works
Robust semantic segmentation based on RGB-thermal in variable lighting scenes
Guo, Z., Xu, L., Xu, Q., & Sun, Z. (2021) · Measurement · doi:10.1016/j.measurement.2021.110176 · Cited by 44 · see citing works
Liu, Y., Gao, K., Wang, H., Yang, Z., Wang, P., Ji, S., Huang, Y., Zhu, Z., & Zhao, X. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104083 · Cited by 43 · see citing works
Adjacent-Scale Multimodal Fusion Networks for Semantic Segmentation of Remote Sensing Data
Ma, X., Xu, X., Zhang, X., & Pun, M.-O. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3486906 · Cited by 44 · see citing works
UCTNet: Uncertainty-Aware Cross-Modal Transformer Network for Indoor RGB-D Semantic Segmentation
Ying, X., & Chuah, M. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_2 · Cited by 42 · see citing works
Global contextually guided lightweight network for RGB-thermal urban scene understanding
Gong, T., Zhou, W., Qian, X., Lei, J., & Yu, L. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105510 · Cited by 43 · see citing works
Multi-source collaborative enhanced for remote sensing images semantic segmentation
Zhao, J., Zhang, D., Shi, B., Zhou, Y. Z., Chen, J., Yao, R., & Xue, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.045 · Cited by 41 · see citing works
On 3D Reconstruction Using RGB-D Cameras
Tychola, K. A., Tsimperidis, I., & Papakostas, G. A. (2022) · Digital · doi:10.3390/digital2030022 · Cited by 41 · see citing works
RCNet: Dual-Network Resonance Collaboration via Mutual Learning for RGB-D Road Defect Detection
Zhou, W., Ju, Z., Cong, R., & Yan, W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3617769 · Cited by 39 · see citing works
Long-Term Localization Using Semantic Cues in Floor Plan Maps
Zimmerman, N., Guadagnino, T., Chen, X., Behley, J., & Stachniss, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3223556 · Cited by 39 · see citing works
UTLNet: Uncertainty-Aware Transformer Localization Network for RGB-Depth Mirror Segmentation
Zhou, W., Cai, Y., Zhang, L., Yan, W., & Yu, L. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3323890 · Cited by 38 · see citing works
Zhao, J., Zhou, Y. Z., Shi, B., Yang, J., Zhang, D., & Yao, R. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3484440 · Cited by 37 · see citing works
Zhou, W., Xiaomin, F., Yan, W., Shan, S., Jiang, Q., & Hwang, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3311480 · Cited by 36 · see citing works
Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation
Cong, R., Yang, D., Chen, R., Wang, S., Cui, Z., & Sheng, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00885 · Cited by 35 · see citing works
Improving Multi-Modal Learning with Uni-Modal TeachersPreprint
Du, C., Li, T., Liu, Y., Wen, Z., Hua, T., Wang, Y., & Zhao, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11059 · Cited by 34 · see citing works
Zhou, W., Jian, B., & Liu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3537281 · Cited by 33 · see citing works
Multi-modal unsupervised domain adaptation for semantic image segmentation
Hu, S., Bonardi, F., Bouchafa, S., & Désiré Sidibé, A. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109299 · Cited by 33 · see citing works
Feng, Z., Guo, Y., & Sun, Y. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3376534 · Cited by 34 · see citing works
WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture
Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023) · Sensors · doi:10.3390/s23052713 · Cited by 31 · see citing works
CNet: A Novel Seabed Coral Reef Image Segmentation Approach Based on Deep Learning
Zhang, H., Li, M., Zhong, J., & Qin, J. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00090 · Cited by 30 · see citing works
Zhou, W., Jian, B., Fang, M., Dong, X., Liu, Y., & Jiang, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3382354 · Cited by 29 · see citing works
EISNet: A Multi-Modal Fusion Network for Semantic Segmentation With Events and Images
Xie, B., Deng, Y., Shao, Z., & Li, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3380255 · Cited by 28 · see citing works
End-to-End Semantic Segmentation Utilizing Multi-Scale Baseline Light Field
Cong, R., Sheng, H., Yang, D., Yang, D., Chen, R., Wang, S., & Cui, Z. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3367370 · Cited by 26 · see citing works
Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems
Moosmann, J., Müller, H., Zimmerman, N., Rutishauser, G., Benini, L., & Magno, M. (2024) · IEEE Access · doi:10.1109/access.2024.3404878 · Cited by 35 · see citing works
Wang, Z., Li, J., Xu, N., & You, Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103207 · Cited by 26 · see citing works
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
Ji, W., Li, J., Bian, C., Zhou, Z., Zhao, J., Yuille, A., & Cheng, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00112 · Cited by 26 · see citing works
Hussain, T., Li, Y., Ren, M., & Li, J. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.141961 · Cited by 25 · see citing works
Link-RGBD: Cross-Guided Feature Fusion Network for RGBD Semantic Segmentation
Wu, P., Guo, R., Tong, X., Su, S., Zuo, Z., Sun, B., & Wei, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3218601 · Cited by 26 · see citing works
ECFNet: Efficient cross-layer fusion network for real time RGB-Thermal urban scene parsing
Shen, Z., Wang, J., Weng, Y., Pan, Z., Li, Y., & Wang, J. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104579 · Cited by 24 · see citing works
Sachs, S., Ratz, M., Mäder, P., König, J., & Cierpka, C. (2023) · Experiments in Fluids · doi:10.1007/s00348-023-03574-2 · Cited by 24 · see citing works
Constructing Metric-Semantic Maps Using Floor Plan Priors for Long-Term Indoor Localization
Zimmerman, N., Sodano, M., Marks, E., Behley, J., & Stachniss, C. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341595 · Cited by 24 · see citing works
Feng, Z., Guo, Y., Navarro-Alarcón, D., Lyu, Y., & Sun, Y. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3272517 · Cited by 23 · see citing works
Efficient Multi-Task Scene Analysis with RGB-D Transformers
Fischedick, S. B., Seichter, D., Schmidt, R., Rabes, L., & Groß, H. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191977 · Cited by 23 · see citing works
Self-citationJung, J., Kim, S., Jang, W., Seo, B., & Lee, K. J. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3349588 · Cited by 23 · see citing works
Robust Multimodal Learning via Representation Decoupling
Wei, S., Luo, Y., Wang, Y., & Luo, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_3 · Cited by 22 · see citing works
VIBUS: Data-efficient 3D scene parsing with VIewpoint Bottleneck and Uncertainty-Spectrum modeling
Tian, B., Luo, L., Zhao, H., & Zhou, G. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.10.013 · Cited by 22 · see citing works
Adjacent Bi-Hierarchical Network for Scene Parsing of Remote Sensing Images
Ma, J., Zhou, W., Lei, J., & Yu, L. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3241648 · Cited by 21 · see citing works
Multi-scale fusion for RGB-D indoor semantic segmentation
Jiang, S., Xu, Y., Li, D., & Fan, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24836-9 · Cited by 20 · see citing works
EFDCNet: Encoding fusion and decoding correction network for RGB-D indoor semantic segmentation
Chen, J., Li, G., Zhang, Z., & Zeng, D. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104892 · Cited by 20 · see citing works
Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation
Zhu, X., Wang, X., Freer, J., Chang, H. J., & Gao, Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160268 · Cited by 20 · see citing works
Guo, Z., Xu, R., Feng, C., & Zeng, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3342477 · Cited by 19 · see citing works
Wang, Y., Zhang, W., Chen, W., Chen, C., & Liang, Z. (2024) · Remote Sensing · doi:10.3390/rs16132459 · Cited by 18 · see citing works
Scale-aware network with modality-awareness for RGB-D indoor semantic segmentation
Zhou, F., Lai, Y., Rosin, P. L., Zhang, F., & Hu, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.025 · Cited by 18 · see citing works
CCFNet: Cross-Complementary fusion network for RGB-D scene parsing of clothing images
Xu, G., Zhou, W., Qian, X., Ye, L., Lei, J., & Yu, L. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103727 · Cited by 18 · see citing works
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers
Rizzoli, G., Shenaj, D., & Zanuttigh, P. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00070 · Cited by 18 · see citing works
Ren, M., Li, Y., Hussain, T., Wu, Y., & Li, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103807 · Cited by 19 · see citing works
The Evolution of Geotech - 25 Years of Innovation
Hammah, R. E., Yacoub, T. E., McQuillan, A., & Curran, J. H. (2021) · CRC Press eBooks · doi:10.1201/9781003188339 · Cited by 17 · see citing works
Jianhui, J., Zhou, W., Ye, L., Lei, J., Yu, L., Qian, X., & Luo, T. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.103087 · Cited by 17 · see citing works
PanopticNDT: Efficient and Robust Panoptic Mapping
Seichter, D., Stephan, B., Fischedick, S. B., Müller, S., Rabes, L., & Groß, H. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342137 · Cited by 17 · see citing works
Self-citationZhou, W., Li, Y., Huan, J., Liu, Y., & Jiang, Q. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3384669 · Cited by 18 · see citing works
Cross-Modal Transformer for RGB-D semantic segmentation of production workshop objects
Ru, Q., Chen, G., Zuo, T., & Liao, X. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109862 · Cited by 16 · see citing works
Li, C., Pan, Y., Li, D., Fan, J., Li, B., Zhao, Y., & Wang, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124586 · Cited by 16 · see citing works
Attention-based fusion network for RGB-D semantic segmentation
Li, Z., Guo, C., Zhan, J., & Deng, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128371 · Cited by 16 · see citing works
Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images
Giang, T. T. H., Khai, T. Q., Im, D.-Y., & Ryoo, Y.-J. (2022) · Sensors · doi:10.3390/s22145140 · Cited by 16 · see citing works
Sui, J., Ma, X., Zhang, X., Pun, M.-O., & Wu, H. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3504569 · Cited by 15 · see citing works
Tian, H., Song, K., Tong, L., Man, Y., & Yan, Y. (2023) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2023.3327865 · Cited by 15 · see citing works
Mobile Robot Oriented Large-Scale Indoor Dataset for Dynamic Scene Understanding
Tang, Y., Tai, C., Chen, F.-X., Zhang, W., Zhang, T., Liu, X., Liu, Y., & Zeng, L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611489 · Cited by 15 · see citing works
Application of Multi-modal Fusion Attention Mechanism in Semantic Segmentation
Liu, Y., Yoshie, O., & Watanabe, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26293-7_23 · Cited by 15 · see citing works
Zhou, W., Jian, B., Liu, Y., & Jiang, Q. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3575088 · Cited by 14 · see citing works
Multi‐Robot Collaborative Complex Indoor Scene Segmentation via Multiplex Interactive Learning
Liu, J., Jiang, Z., Xu, X., Li, W., Liu, M., & Liu, H. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70066 · Cited by 14 · see citing works
Zhou, W., Xie, J., & Xu, C. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3675381 · Cited by 14 · see citing works
Zhou, W., Xiaomin, F., Yu, L., & Lei, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3243247 · Cited by 14 · see citing works
Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks
Barchid, S., Mennesson, J., & Djéraba, C. (2021) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi50038.2021.9461875 · Cited by 14 · see citing works
Efficient and Robust Semantic Mapping for Indoor Environments
Seichter, D., Langer, P., Wengefeld, T., Lewandowski, B., Hochemer, D., & Groß, H. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812205 · Cited by 14 · see citing works
Self-citationZhang, D., Yue, P., Yan, Y., Niu, Q., Zhao, J., & Ma, H. (2024) · Remote Sensing · doi:10.3390/rs16244717 · Cited by 13 · see citing works
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
Sodano, M., Magistri, F., Guadagnino, T., Behley, J., & Stachniss, C. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160315 · Cited by 13 · see citing works
TCANet: three-stream coordinate attention network for RGB-D indoor semantic segmentation
Jia, W., Yan, X., Liu, Q., Zhang, T., & Dong, X. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01210-4 · Cited by 13 · see citing works
S2Net: A Multitask Learning Network for Semantic Stereo of Satellite Image Pairs
Liao, P., Zhang, X., Chen, G., Wang, T., Li, X., Yang, H., Zhou, W., He, C., & Wang, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3335997 · Cited by 13 · see citing works
Morphology-Guided Network via Knowledge Distillation for RGB-D Mirror Segmentation
Zhou, W., Cai, Y., & Qiang, F. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3404654 · Cited by 13 · see citing works
- 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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