Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

U-Net: Convolutional Networks for Biomedical Image Segmentation

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Deep learning

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ImageNet: A large-scale hierarchical image database

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Fully convolutional networks for semantic segmentation

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Deep learning in neural networks: an overview

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Are we ready for autonomous driving? The KITTI vision benchmark suite

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A survey on Image Data Augmentation for Deep Learning

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The Cityscapes Dataset for Semantic Urban Scene Understanding

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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint

Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 10,099

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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Domain-Adversarial Training of Neural Networks

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

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nuScenes: A Multimodal Dataset for Autonomous Driving

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The Pascal Visual Object Classes Challenge: A Retrospective

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Prototypical Networks for Few-shot LearningPreprint

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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201

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

Context Encoders: Feature Learning by Inpainting

Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691

3D is here: Point Cloud Library (PCL)

Rusu, R., & Cousins, S. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 5,513

Learning Transferable Features with Deep Adaptation Networks

Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Tarvainen, A., & Valpola, H. (2017) · arXiv · doi:10.48550/arxiv.1703.01780 · Cited by 4,275

CARLA: An open urban driving simulator

Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718

Multi-view 3D Object Detection Network for Autonomous Driving

Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00252 · Cited by 3,437

Colorful Image Colorization

Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,417

KPConv: Flexible and Deformable Convolution for Point Clouds

Thomas, H., Qi, C. R., Deschaud, J., Marcotegui, B., Goulette, F., & Guibas, L. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,400

LOAM: Lidar Odometry and Mapping in Real-time

Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303

A Survey on Deep Transfer Learning

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775

A survey on deep learning techniques for image and video semantic segmentation

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Unsupervised Visual Representation Learning by Context Prediction

Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629

Deep Learning for 3D Point Clouds: A Survey

Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3005434 · Cited by 2,575

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484

Unbiased look at dataset bias

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The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00939 · Cited by 2,294

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01112 · Cited by 2,275

Stanley: The robot that won the DARPA Grand Challenge

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Real-time loop closure in 2D LIDAR SLAM

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Playing for Data: Ground Truth from Computer Games

Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_7 · Cited by 1,981

PointConv: Deep Convolutional Networks on 3D Point Clouds

Wu, W., Qi, Z., & Li, F. (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.2019.00985 · Cited by 1,972

3D Semantic Parsing of Large-Scale Indoor Spaces

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Boosting for transfer learning

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3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00961 · Cited by 1,788

Panoptic Segmentation

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Toward Open Set Recognition

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Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

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ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

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Argoverse: 3D Tracking and Forecasting With Rich Maps

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Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training

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Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

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RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

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Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

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Big data deep learning: Challenges and perspectives

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Domain-adversarial training of neural networks

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ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

Lin, D., Dai, J., Jia, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.344 · Cited by 1,129

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

Dai, J., He, K., & Sun, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.191 · Cited by 1,124

A Review on Deep Learning Techniques Applied to Semantic SegmentationPreprint

García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., & García‐Rodríguez, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.06857 · Cited by 1,049

SO-Net: Self-Organizing Network for Point Cloud Analysis

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Unsupervised Learning of Visual Representations Using Videos

Wang, X., & Gupta, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.320 · Cited by 1,005

What’s the Point: Semantic Segmentation with Point Supervision

Bearman, A., Russakovsky, O., Ferrari, V., & Fei-Fei, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_34 · Cited by 999

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967

One-Shot Learning for Semantic Segmentation

Shaban, A., Bansal, S., Liu, Z., Essa, I. A., & Boots, B. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.167 · Cited by 958

SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

Häckel, T., Savinov, N., Ladický, Ľ., Wegner, J. D., Konrad Schindler, B., & Pollefeys, M. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-w1-91-2017 · Cited by 928

SEGCloud: Semantic Segmentation of 3D Point Clouds

Tchapmi, L. P., Choy, C., Armeni, I., Gwak, J., & Savarese, S. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00067 · Cited by 853

Deep Learning: A Critical Appraisal

Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M., & Kautz, J. (2018) · 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.2018.00268 · Cited by 804

Graph Attention Convolution for Point Cloud Semantic Segmentation

Wang, L., Huang, Y., Hou, Y., Zhang, S., & Shan, J. (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.2019.01054 · Cited by 801

SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Wu, B., Zhou, X., Zhao, S., Yue, X., & Keutzer, K. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793495 · Cited by 785

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

Kolesnikov, A., & Lampert, C. H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_42 · Cited by 773

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

Weinmann, M., Jutzi, B., Hinz, S., & Mallet, C. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.01.016 · Cited by 742

CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning

Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (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.2019.00536 · Cited by 716

From image-level to pixel-level labeling with Convolutional Networks

Pinheiro, P. O., & Collobert, R. (2015) · 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.2015.7298780 · Cited by 703

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

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PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

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Unity: A General Platform for Intelligent Agents

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Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

Pathak, D., Krähenbühl, P., & Darrell, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.209 · Cited by 619

The ApolloScape Open Dataset for Autonomous Driving and Its Application

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Tangent Convolutions for Dense Prediction in 3D

Tatarchenko, M., Park, J., Koltun, V., & Zhou, Q.-Y. (2018) · 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.2018.00409 · Cited by 592

Survey on semantic segmentation using deep learning techniques

Lateef, F., & Ruichek, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.003 · Cited by 577

Vehicle Detection from 3D Lidar Using Fully Convolutional Network

Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577

Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation

Xie, Y., Tian, J., & Zhu, X. X. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2937630 · Cited by 542

SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds

Cortinhal, T., Tzelepis, G., & Aksoy, E. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64559-5_16 · Cited by 513

Recurrent Slice Networks for 3D Segmentation of Point Clouds

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Undoing the Damage of Dataset Bias

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Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling

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Learning Features by Watching Objects Move

Pathak, D., Girshick, R., Dollár, P., Darrell, T., & Hariharan, B. (2017) · 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.2017.638 · Cited by 458

Playing for Benchmarks

Richter, S. R., Hayder, Z., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.243 · Cited by 451

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

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A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

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3D point cloud segmentation: A survey

Nguyen, A., & Le, B. H. (2013) · IEEE Conference on Robotics, Automation and Mechatronics (RAM) · doi:10.1109/ram.2013.6758588 · Cited by 437

ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics

Zhang, Z., Hua, B., & Yeung, S.-K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00169 · Cited by 435

Review: Deep Learning on 3D Point Clouds

Bello, S. A., Yu, S., Wang, C., Adam, J. M., & Li, J. (2020) · Remote Sensing · doi:10.3390/rs12111729 · Cited by 433

Shape-based recognition of 3D point clouds in urban environments

Golovinskiy, A., Kim, V. G., & Funkhouser, T. A. (2009) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459471 · Cited by 429

Few-Shot Semantic Segmentation with Prototype Learning.

Dong, N., & Xing, E. P. (2018) · Cited by 383

FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

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Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data

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DAR-Net: Dynamic Aggregation Network for Semantic Scene SegmentationPreprint

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