The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
Abstract
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images, thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this paper, we propose to use a virtual world to automatically generate realistic synthetic images with pixel-level annotations. Then, we address the question of how useful such data can be for semantic segmentation - in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show how the inclusion of SYNTHIA in the training stage significantly improves performance on the semantic segmentation task.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fully synthetic datasets such as SYNTHIA and StreetHazards have been adopted for out-of-distribution detection as well as domain adaptation and generalization.
“Fully synthetic datasets such as SYNTHIA (Ros et al., 2016) and StreetHazards (Hendrycks et al., 2020a) have been adopted for out-of-distribution detection as well as domain adaptation and generalization, e.g., by testing robustness to transformations in the seasons, weather, time, or architectural style (Hoffman et al., 2018; Volpi et al., 2018).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Ros et al., 2016
Synthia is one of the oldest multimodal synthetic datasets providing labeled semantic segmentation samples.
“Synthia [70-72] is one of the oldest multimodal synthetic datasets providing labeled semantic segmentation samples.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [70]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · 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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · see citing works
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · 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
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210 · see citing works
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A. W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · 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.2011.5995316 · Cited by 3,545 · see citing works
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works
The Role of Context for Object Detection and Semantic Segmentation in the Wild
Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.119 · Cited by 1,438 · see citing works
Semantic object classes in video: A high-definition ground truth database
Brostow, G., Fauqueur, J., & Cipolla, R. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.04.005 · Cited by 1,400 · see citing works
Joint Training of a Convolutional Network and a Graphical Model for Human Pose EstimationPreprint
Tompson, J., Jain, A., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2984 · Cited by 1,317 · see citing works
Large-scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G. D., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · Cited by 1,266 · see citing works
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050 · see citing works
Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models
Aubry, M., Maturana, D., Efros, A. A., Russell, B. C., & Šivic, J. (2014) · 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.2014.487 · Cited by 471 · see citing works
Learning Deep Object Detectors from 3D Models
Peng, X., Sun, B., Ali, K., & Saenko, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.151 · Cited by 336 · see citing works
Cordts, M., Omran, M., Ramos, S., Scharwächter, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2015) · Cited by 271 · see citing works
Articulated people detection and pose estimation: Reshaping the future
Pishchulin, L., Jain, A., Andriluka, M., Thormählen, T., & Schiele, B. (2012) · 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.2012.6248052 · Cited by 228 · see citing works
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video
Kundu, A., Li, Y., Dellaert, F., Li, F., & Rehg, J. M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_45 · Cited by 220 · see citing works
Virtual and Real World Adaptation for Pedestrian Detection
Vázquez, D., López, A. M., Marín, J., Ponsa, D., & Gerónimo, D. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.163 · Cited by 177 · see citing works
Learning appearance in virtual scenarios for pedestrian detection
Marín, J., Vázquez, D., Gerónimo, D., & López, A. M. (2010) · 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.2010.5540218 · Cited by 170 · see citing works
From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains
Sun, B., & Saenko, K. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.82 · Cited by 156 · see citing works
Domain Adaptation of Deformable Part-Based Models
Xu, J.-L., Ramos, S., Vázquez, D., & López, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2327973 · Cited by 153 · see citing works
Learning scene-specific pedestrian detectors without real data
Hattori, H., Boddeti, V. N., Kitani, K., & Takeo Kanade, A. (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.2015.7299006 · Cited by 134 · see citing works
Vision-Based Offline-Online Perception Paradigm for Autonomous Driving
Ros, G., Ramos, S., Granados, M., Bakhtiary, A., Vázquez, D., & López, A. M. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.38 · Cited by 123 · see citing works
Self-citationEfficient Multi-cue Scene Segmentation
Scharwächter, T., Enzweiler, M., Franke, U., & Roth, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40602-7_46 · Cited by 87 · see citing works
Semantic Pose Using Deep Networks Trained on Synthetic RGB-D
Papon, J. A., & Schoeler, M. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.95 · Cited by 57 · see citing works
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise LabellingPreprint
Badrinarayanan, V., Handa, A., & Cipolla, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07293 · Cited by 57 · see citing works
Hierarchical Adaptive Structural SVM for Domain Adaptation
Xu, J., Ramos, S., Vázquez, D., & López, A. M. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0885-6 · Cited by 38 · see citing works
Training Constrained Deconvolutional Networks for Road Scene Semantic SegmentationPreprint
Ros, G., Stent, S., Alcantarilla, P. F., & Watanabe, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01545 · Cited by 26 · see citing works
Self-citationBerg, T. L., Sorokin, A. A., Wang, G., Forsyth, D. A., Hoiem, D., Endres, I., & Farhadi, A. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2032355 · Cited by 22 · see citing works
Adaptation of Synthetic Data for Coarse-to-Fine Viewpoint Refinement
Busto, P. P., Liebelt, J., & Gall, J. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.14 · Cited by 19 · see citing works
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
Handa, A., Pătrăucean, V., Badrinarayanan, V., Stent, S., & Cipolla, R. (2015) · Apollo (University of Cambridge) · doi:10.17863/cam.26487 · Cited by 11 · see citing works
Flagged first, then by citations
5 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
“tion labels and [4, 38] providing raw data without semantic labels. An alternative to collecting real-world multimodal driving data is by generating synthetic data via simulation. CARLA [16], SYNTHIA [47], and Virtual KITTI [21] simulate virtual cities using game engines. Playing for Benchmarks [45] retrieves renderin…”
MethodImage 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
Multimodal Unsupervised Image-to-Image Translation
Huang, X., Liu, M.-Y., Belongie, S., & Kautz, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_11 · Cited by 2,597 · see citing works
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 · see citing works
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Saito, K., Watanabe, K., Ushiku, Y., & Harada, T. (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.2018.00392 · Cited by 2,278 · see citing works
Unsupervised Image-to-Image Translation NetworksPreprint
Liu, M.-Y., Breuel, T. M., & Kautz, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00848 · Cited by 2,121 · see citing works
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
Choy, C., Gwak, J., & Savarese, 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.2019.00319 · Cited by 2,067 · see citing works
Learning from Simulated and Unsupervised Images through Adversarial Training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (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.2017.241 · Cited by 1,831 · see citing works
Learning to Adapt Structured Output Space for Semantic Segmentation
Tsai, Y., Hung, W.-C., Schulter, S., Sohn, K., Yang, M., & Chandraker, M. K. (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.2018.00780 · Cited by 1,753 · see citing works
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Jégou, S., Drozdzal, M., Vázquez, D., Romero, A., & Bengio, Y. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.156 · Cited by 1,762 · see citing works
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533 · 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
A survey on deep learning techniques for image and video semantic segmentation
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., Martínez-González, P., & García‐Rodríguez, J. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.018 · Cited by 2,488 · see citing works
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training
Zou, Y., Yu, Z., Vijaya Kumar, B. V. K., & Wang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_18 · Cited by 1,464 · see citing works
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Neuhold, G., Ollmann, T., Bulo, S. R., & Kontschieder, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.534 · Cited by 1,434 · see citing works
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
Semantic Foggy Scene Understanding with Synthetic Data
Sakaridis, C., Dai, D., & Gool, L. V. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1072-8 · Cited by 1,354 · see citing works
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315 · see citing works
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019) · Engineering · doi:10.1016/j.eng.2018.11.030 · Cited by 1,235 · see citing works
FDA: Fourier Domain Adaptation for Semantic Segmentation
Yang, Y., & Soatto, S. (2020) · 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/cvpr42600.2020.00414 · Cited by 1,186 · see citing works
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 · see citing works
Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972 · see citing works
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046 · see citing works
Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., & Geiger, A. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00012 · Cited by 924 · see citing works
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., & Birchfield, S. T. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00143 · Cited by 936 · see citing works
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891 · see citing works
Confidence Regularized Self-Training
Zou, Y., Yu, Z., Liu, X., Kumar, B. V. K. V., & Wang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00608 · Cited by 863 · see citing works
Luo, Y., Zheng, L., Guan, T., Yu, J., & Yang, Y. (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.2019.00261 · Cited by 837 · see citing works
Block-NeRF: Scalable Large Scene Neural View Synthesis
Tancik, M., Casser, V., Yan, X., Pradhan, S., Mildenhall, B., Srinivasan, P. P., Barron, J. T., & Kretzschmar, H. (2022) · 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/cvpr52688.2022.00807 · Cited by 808 · see citing works
Gibson Env: Real-World Perception for Embodied Agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763 · see citing works
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
Liao, Y., Xie, J., & Geiger, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179507 · Cited by 770 · see citing works
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Li, Y., Yuan, L., & Vasconcelos, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00710 · Cited by 704 · see citing works
The ApolloScape Dataset for Autonomous Driving
Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., & Yang, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00141 · Cited by 679 · see citing works
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
Dwibedi, D., Misra, I., & Hebert, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.146 · Cited by 687 · see citing works
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & de Albuquerque, V. H. C. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032227 · Cited by 675 · see citing works
Hoyer, L., Dai, D., & Gool, L. V. (2022) · 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/cvpr52688.2022.00969 · Cited by 663 · see citing works
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Xie, S., Gu, J., Guo, D., Qi, C. R., Guibas, L. J., & Litany, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_34 · Cited by 655 · see citing works
Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?
Johnson‐Roberson, M., Barto, C., Mehta, R., Sridhar, S. N., Rosaen, K., & Vasudevan, R. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989092 · Cited by 649 · see citing works
Computer Vision for Autonomous Vehicles
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000079 · Cited by 664 · see citing works
FCNs in the Wild: Pixel-level Adversarial and Constraint-based AdaptationPreprint
Hoffman, J., Wang, D., Yu, F., & Darrell, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02649 · Cited by 692 · see citing works
Learning From Synthetic Data for Crowd Counting in the Wild
Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00839 · Cited by 654 · see citing works
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
Prakash, A., Chitta, K., & Geiger, A. (2021) · 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/cvpr46437.2021.00700 · Cited by 616 · see citing works
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
Lee, C., Batra, T., Baig, M. H., & Ulbricht, D. (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.2019.01053 · Cited by 610 · see citing works
Perception and sensing for autonomous vehicles under adverse weather conditions: A survey
Zhang, Y., Carballo, A., Yang, H., & Takeda, K. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.021 · Cited by 640 · see citing works
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Pohlen, T., Hermans, A., Mathias, M., & Leibe, 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.2017.353 · Cited by 591 · see citing works
A Brief Survey on Semantic Segmentation with Deep Learning
Hao, S., Zhou, Y., & Guo, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.118 · Cited by 608 · see citing works
AI2-THOR: An Interactive 3D Environment for Visual AI
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Deitke, M., Ehsani, K., Gordon, D., Zhu, Y., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.05474 · Cited by 578 · see citing works
Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., & Wen, F. (2021) · 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/cvpr46437.2021.01223 · Cited by 575 · see citing works
ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding
Sakaridis, C., Dai, D., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01059 · Cited by 572 · see citing works
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., Yuan, J., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02067 · Cited by 577 · see citing works
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
Kemker, R., Salvaggio, C., & Kanan, C. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.014 · Cited by 558 · see citing works
Survey on semantic segmentation using deep learning techniques
Lateef, F., & Ruichek, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.003 · Cited by 561 · see citing works
Image to Image Translation for Domain Adaptation
Murez, Z., Kolouri, S., Kriegman, D. J., Ramamoorthi, R., & Kim, K. (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.2018.00473 · Cited by 543 · see citing works
VisDA: The Visual Domain Adaptation ChallengePreprint
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., & Saenko, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.06924 · Cited by 667 · see citing works
Zheng, Z., & Yang, Y. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01395-y · Cited by 542 · see citing works
UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss
Meister, S., Hur, J., & Roth, S. (2018) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v32i1.12276 · Cited by 535 · see citing works
TransFuser: Imitation With Transformer-Based Sensor Fusion for Autonomous Driving
Chitta, K., Prakash, A., Jaeger, B., Yu, Z., Renz, K., & Geiger, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3200245 · Cited by 528 · see citing works
DeepMVS: Learning Multi-view Stereopsis
Huang, P., Matzen, K., Kopf, J., Ahuja, N., & Huang, 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.2018.00298 · Cited by 514 · see citing works
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
Zhang, Y., David, P., & Gong, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.223 · Cited by 504 · see citing works
BlendedMVS: A Large-Scale Dataset for Generalized Multi-View Stereo Networks
Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., & Quan, L. (2020) · 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/cvpr42600.2020.00186 · Cited by 502 · see citing works
DF-Net: Unsupervised Joint Learning of Depth and Flow Using Cross-Task Consistency
Zou, Y., Luo, Z., & Huang, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_3 · Cited by 484 · see citing works
The ApolloScape Open Dataset for Autonomous Driving and Its Application
Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., & Yang, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2926463 · Cited by 615 · see citing works
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., & Chellappa, R. (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.2018.00395 · Cited by 488 · see citing works
Understanding Deep Learning Techniques for Image Segmentation
Ghosh, S., Das, N., Das, I., & Maulik, U. (2019) · ACM Computing Surveys · doi:10.1145/3329784 · Cited by 492 · see citing works
Generalizing to Unseen Domains via Adversarial Data AugmentationPreprint
Volpi, R., Namkoong, H., Şener, O., Duchi, J. C., Murino, V., & Savarese, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12018 · Cited by 581 · see citing works
Learning to Learn Single Domain Generalization
Qiao, F., Zhao, L., & Peng, X. (2020) · 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/cvpr42600.2020.01257 · Cited by 476 · see citing works
3D Object Detection for Autonomous Driving: A Comprehensive Survey
Mao, J., Shi, S., Wang, X., & Li, H. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01790-1 · Cited by 492 · see citing works
Deep learning for monocular depth estimation: A review
Ming, Y., Meng, X., Fan, C., & Yu, H. L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.12.089 · Cited by 466 · see citing works
Richter, S. R., Hayder, Z., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.243 · Cited by 434 · see citing works
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
Gehrig, M., Aarents, W., Gehrig, D., & Scaramuzza, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3068942 · Cited by 444 · see citing works
Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes
Alhaija, H. A., Mustikovela, S. K., Mescheder, L., Geiger, A., & Rother, C. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1070-x · Cited by 434 · see citing works
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Zhu, Y., Sapra, K., Reda, F. A., Shih, K. J., Newsam, S. D., Tao, A., & Catanzaro, B. (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.2019.00906 · Cited by 429 · see citing works
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Tranheden, W., Olsson, V., Pinto, J., & Svensson, L. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00142 · Cited by 425 · see citing works
Encoder–decoder network for pixel-level road crack detection in black-box images
Bang, S., Park, S., Kim, H., & Kim, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12440 · Cited by 421 · see citing works
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 434 · see citing works
Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision
Pan, F., Shin, I., Rameau, F., Lee, S., & Kweon, I. S. (2020) · 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/cvpr42600.2020.00382 · Cited by 407 · see citing works
Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A. L., Keutzer, K., & Gong, B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00219 · Cited by 387 · see citing works
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
Hoyer, L., Dai, D., Wang, H., & Gool, L. V. (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/cvpr52729.2023.01128 · Cited by 381 · see citing works
DLOW: Domain Flow for Adaptation and Generalization
Gong, R., Li, W., Chen, Y., & Gool, L. V. (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.2019.00258 · Cited by 368 · see citing works
IQA: Visual Question Answering in Interactive Environments
Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., & Farhadi, A. (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.2018.00430 · Cited by 362 · see citing works
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Zhao, S., Yue, X., Zhang, S., Li, B., Zhao, H., Wu, B., Krishna, R., Gonzalez, J. E., Sangiovanni-Vincentelli, A. L., Seshia, S. A., et al. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3028503 · Cited by 355 · see citing works
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01073 · Cited by 358 · see citing works
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
Chen, Y., Chen, W., Chen, Y.-T., Tsai, B.-C., Wang, Y.-C. F., & Sun, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.220 · Cited by 345 · see citing works
Choi, S., Jung, S., Yun, H., Kim, J., Kim, S., & Choo, J. (2021) · 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/cvpr46437.2021.01141 · Cited by 341 · see citing works
Domain Adaptation for Structured Output via Discriminative Patch Representations
Tsai, Y., Sohn, K., Schulter, S., & Chandraker, M. K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00154 · Cited by 330 · see citing works
Rahimian, F. P., Seyedzadeh, S., Oliver, S., Rodríguez, S., & Dawood, N. N. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.103012 · Cited by 347 · see citing works
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
Li, Y., Ma, D., An, Z., Wang, Z., Zhong, Y., Chen, S., & Feng, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3192802 · Cited by 337 · see citing works
Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation
Wang, H., Shen, T., Zhāng, W., Duan, L., & Mei, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_38 · Cited by 326 · see citing works
Domain Adaptation for Visual Applications: A Comprehensive SurveyPreprint
Csurka, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05374 · Cited by 324 · see citing works
Domain Adaptation for Semantic Segmentation With Maximum Squares Loss
Chen, M., Xue, H., & Cai, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00218 · Cited by 316 · see citing works
Street-view change detection with deconvolutional networks
Alcantarilla, P. F., Stent, S., Ros, G., Arroyo, R., & Gherardi, R. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9734-5 · Cited by 323 · see citing works
Self-citationConditional Generative Adversarial Network for Structured Domain Adaptation
Hong, W., Wang, Z., Yang, M., & Yuan, 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.2018.00145 · Cited by 313 · see citing works
CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency
Chen, Y., Lin, Y., Yang, M., & Huang, 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.2019.00189 · Cited by 306 · see citing works
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Jaritz, M., de Charette, R., Wirbel, É., Perrotton, X., & Nashashibi, F. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00017 · Cited by 311 · see citing works
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
Hoyer, L., Dai, D., & Gool, L. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_22 · Cited by 309 · see citing works
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 303 · see citing works
“There are several popular synthetic datasets like SYNTHIA [44] and CARLA [13].”
MethodROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Chen, Y., Li, W., & Gool, L. V. (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.2018.00823 · Cited by 303 · see citing works
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Luo, C., Yang, Z., Wang, P., Wang, Y., Xu, W., Nevatia, R., & Yuille, A. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2930258 · Cited by 306 · see citing works
FSDR: Frequency Space Domain Randomization for Domain Generalization
Huang, J., Guan, D., Xiao, A., & Lu, S. (2021) · 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/cvpr46437.2021.00682 · Cited by 302 · see citing works
Mukhamediev, R. I., Popova, Y., Kuchin, Y. I., Zaitseva, E., Kalimoldayev, A. M., Сымагулов, А., Levashenko, V., Abdoldina, F., Gopejenko, V. I., Yakunin, K., et al. (2022) · Mathematics · doi:10.3390/math10152552 · Cited by 360 · see citing works
- Authors
- Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M.
- Affiliations
- Computer Vision Center (ES)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3234-3243
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2431874326
- Cited by
- 2,338
- References
- 45
- Citations by year
- 2026: 98 · 2025: 200 · 2024: 272 · 2023: 316 · 2022: 305 · 2021: 361 · 2020: 284 · 2019: 239 · 2018: 163 · 2017: 89 · 2016: 11
- FWCI
- 57.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- 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 › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- synthetic images, semantic segmentation, urban scenes, autonomous driving, virtual worlds, pixel-level annotation, deep convolutional neural networks, data augmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
