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Analyzing scenes thoroughly is crucial for mobile robots acting in different environments.
H.-M. Gross, et al. , “TOOMAS: Interactie shopping guide robots in everyday use – final implementation and experiences from long-term field trials,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2009, pp. 2005–2012
2012
Earlier work this paper cites.
N. Silberman, et al. , “Indoor Segmentation and Support Inference from RGBD Images,” in Europ. Conf. on Computer Vision (ECCV) , 2012
2012
Earlier work this paper cites.
E. Einhorn and H.-M. Gross, “Generic 2D/3D SLAM with NDT maps for lifelong application,” in Europ. Conf. on Mobile Robots (ECMR) , 2013
2013
Earlier work this paper cites.
H.-M. Gross, et al. , “Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 5992–5999
2015
Earlier work this paper cites.
S. Song, et al. , “SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 567–576
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in Int. Conf. Learning Representation (ICLR) , 2015
2015
Earlier work this paper cites.
D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” Int. Conf. on Computer Vision (ICCV) , pp. 2650–2658, 2015
2015
Earlier work this paper cites.
C. Hazirbas, et al. , “FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture,” in Asian Conference on Computer Vision (ACCV) , 2016, pp. 213–228
2016
Earlier work this paper cites.
M. Cordts, et al. , “The Cityscapes Dataset for Semantic Urban Scene Understanding,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , pp. 3213–3223, 2016
2016
Earlier work this paper cites.
K. He, et al. , “Deep residual learning for image recognition,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2016
2016
Earlier work this paper cites.
Lingzhu Xiang, et al. , “Libfreenect2: Release 0.2,” 2016. [Online]. Available: https://zenodo.org/record/50641
2016
Earlier work this paper cites.
F. J. Lawin, et al. , “Efficient multi-frequency phase unwrapping using kernel density estimation,” in Europ. Conf. on Computer Vision (ECCV) , 2016, pp. 170–185
2016
Earlier work this paper cites.
H.-M. Gross, et al. , “Mobile robot companion for walking training of stroke patients in clinical post-stroke rehabilitation,” in IEEE Int. Conf. on Robotics and Automation (ICRA) , 2017, pp. 1028–1035
2017
Earlier work this paper cites.
S. Lee, et al. , “RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation,” Int. Conference on Computer Vision (ICCV) , pp. 4990–4999, 2017
2017
Earlier work this paper cites.
H. Zhao, et al. , “Pyramid scene parsing network,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 2881–2890
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. McCormac, et al. , “SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?” Int. Conf. on Computer Vision (ICCV) , pp. 2697–2706, 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Zhong, et al. , “3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes,” in Int. Conf. on Pattern Recognition (ICPR) , 2018, pp. 2343–2349
2018
Cited alongside, same era.
W. Wang and U. Neumann, “Depth-Aware CNN for RGB-D Segmentation,” in Europ. Conf. on Computer Vision (ECCV) , 2018, pp. 144–161
2018
Cited alongside, same era.
E. Romera, et al. , “ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation,” IEEE Transactions on Intelligent Transportation Systems (ITS) , pp. 263–272, 2018
Y. Wang, et al. , “LEDnet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation,” in IEEE Int. Conference on Image Processing (ICIP) , 2019, pp. 1860–1864
2019
Later among the works it cites.
G. Li, et al. , “DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation,” British Machine Vision Conference (BMVC) , 2019
2019
Later among the works it cites.
S.-Y. Lo, et al. , “Efficient dense modules of asymmetric convolution for real-time semantic segmentation,” in ACM Int. Conf. on Multimedia in Asia , 2019, pp. 1–6
2019
Later among the works it cites.
M. Oršić, et al. , “In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , pp. 12 607–12 616, 2019
2019
Later among the works it cites.
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2018
Cited alongside, same era.
C. Yu, et al. , “BiSeNet: Bilateral segmentation network for real-time semantic segmentation,” in Europ. Conf. on Computer Vision (ECCV) , 2018, pp. 325–341
2018
Cited alongside, same era.
J. Hu, et al. , “Squeeze-and-excitation networks,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 7132–7141
2018
Cited alongside, same era.
L.-C. Chen, et al. , “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” in Europ. Conf. on Computer Vision (ECCV) , 2018, pp. 801–818
2018
Cited alongside, same era.
H. M. Gross, et al. , “Living with a mobile companion robot in your own apartment - final implementation and results of a 20-weeks field study with 20 seniors,” in IEEE Int. Conf. on Robotics and Automation (ICRA), Montreal, Canada . IEEE, 2019, pp. 2253–2259
2019
Cited alongside, same era.
X. Hu, et al. , “ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation,” IEEE Int. Conf. on Image Processing (ICIP) , 2019
2019
Cited alongside, same era.
Y. Xing, et al. , “2.5D Convolution for RGB-D Semantic Segmentation,” in IEEE Int. Conf. on Image Processing (ICIP) , 2019, pp. 1410–1414
2019
Cited alongside, same era.
Y. Chen, et al. , “3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation,” in Int. Conf. on 3D Vision (3DV) , 2019, pp. 173–182
2019
Cited alongside, same era.
S. Mehta, et al. , “ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 9190–9200
2019
Later among the works it cites.
A. Paszke, et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (NIPS) . Curran Associates, Inc., 2019, pp. 8024–8035
2019
Later among the works it cites.
J. Bai, et al. , “Onnx: Open neural network exchange,” https://github.com/onnx/onnx , 2019
2019
Later among the works it cites.
S.-W. Hung, et al. , “Incorporating Luminance, Depth and Color Information by a Fusion-Based Network for Semantic Segmentation,” in IEEE Int. Conf. on Image Processing (ICIP) , 2019, pp. 2374–2378
2019
Later among the works it cites.
B. Lewandowski, et al. , “Socially compliant human-robot interaction for autonomous scanning tasks in supermarket environments,” in IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN) . IEEE, 2020, pp. 363–370
2020
Closest in time.
T. Q. Trinh, et al. , “Autonomous mobile gait training robot for orthopedic rehabilitation in a clinical environment*,” in IEEE Int. Conf. on Robot and Human Interactive Communication (RO-MAN) , 2020, pp. 580–587
2020
Closest in time.
D. Seichter, et al. , “Multi-task deep learning for depth-based person perception in mobile robotics,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 497–10 504
2020
Closest in time.
Y. Xing, et al. , “Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing,” in Europ. Conf. on Computer Vision (ECCV) , 2020, pp. 1–17
2020
Closest in time.
2020
Closest in time.
X. Chen, et al. , “Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation,” in Europ. Conf. on Computer Vision (ECCV) , 2020, pp. 561–577
2020
Closest in time.
J. Bornschein, et al. , “Small Data, Big Decisions: Model Selection in the Small-Data Regime,” in Int. Conf. on Machine Learning (ICML) , 2020
2020
Closest in time.