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Understanding the semantic characteristics of the environment is a key enabler for autonomous robot operation.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Rigid scene flow for 3d lidar scans
Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, and Wolfram Burgard · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Deep semantic classification for 3d lidar data
Ayush Dewan, Gabriel L Oliveira, and Wolfram Burgard · 2017
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The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Bengio · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Semantics-aware visual localization under challenging perceptual conditions
Tayyab Naseer, Gabriel L Oliveira, Thomas Brox, and Wolfram Burgard · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Vlocnet++: Deep multitask learning for semantic visual localization and odometry
Noha Radwan, Abhinav Valada, and Wolfram Burgard · 2018
Later among the works it cites.
Mapping with dynamic-object probabilities calculated from single 3d range scans
Philipp Ruchti and Wolfram Burgard · 2018
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SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, and Jan Kautz · 2018
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Tangent Convolutions for Dense Prediction in 3D
Maxim Tatarchenko, Jaesik Park, Vladen Koltun, and Qian-Yi Zhou · 2018
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Pointseg: Real-time semantic segmentation based on 3d lidar point cloud
Yuan Wang, Tianyue Shi, Peng Yun, Lei Tai, and Ming Liu · 2018
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Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
Loic Landrieu and Martin Simonovsky · 2018
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas
Cited in the paper.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas
Cited in the paper.
Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer
Cited in the paper.
Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer · 2018
Later among the works it cites.
https://waymo.com/mission/
Waymo: Our Mission · 2019
Closest in time.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall · 2019
Closest in time.