A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
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In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · 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
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392 · see citing works
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163 · 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
(2017) · Cited by 11,625 · 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
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 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433 · see citing works
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154 · see citing works
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 · see citing works
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 · 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
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847 · see citing works
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., & Darrell, T. (2017) · arXiv · doi:10.48550/arxiv.1711.03213 · Cited by 2,656 · see citing works
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 1× · see citing works
“In addition, the combination of FCNs and structure models, such as conditional random field (CRF), have been thoroughly studied, either as a post-processing step [10] or as part of the network [33, 36, 77], enabling end-to-end training.”
BackgroundRos, 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 · see citing works
Pixelwise View Selection for Unstructured Multi-View Stereo
Schönberger, J. L., Zheng, E., Frahm, J., & Pollefeys, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_31 · Cited by 2,331 · see citing works
Self-citationPlaying 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 1,928 · see citing works
Matterport3D: Learning from RGB-D Data in Indoor Environments
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858 · 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
1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632 · 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
Attention to Scale: Scale-Aware Semantic Image Segmentation
Chen, L.-C., Yang, Y., Wang, J., Xu, W., & Yuille, A. (2016) · 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.2016.396 · Cited by 1,462 · 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
Unsupervised Domain Adaptation with Residual Transfer NetworksPreprint
Long, M., Han, Z., Wang, J., & Jordan, M. I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04433 · Cited by 1,282 · see citing works
Semantic Scene Completion from a Single Depth Image
Song, S., Yu, F., Zeng, A., Chang, A. X., Savva, M., & Funkhouser, T. A. (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.2017.28 · Cited by 1,244 · see citing works
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 1,118 · see citing works
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 986 · 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
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
Xiao, J., Owens, A., & Torralba, A. B. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.458 · Cited by 805 · see citing works
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Khoreva, A., Benenson, R., Hosang, J., Hein, M., & Schiele, 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.2017.181 · Cited by 781 · see citing works
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Sattler, T., Maddern, W., Toft, C., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., Sivic, J., et al. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00897 · Cited by 699 · see citing works
Self-citationWhat you saw is not what you get: Domain adaptation using asymmetric kernel transforms
Kulis, B., Saenko, K., & Darrell, T. (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.2011.5995702 · Cited by 698 · 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
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 691 · see citing works
SemanticFusion: Dense 3D semantic mapping with convolutional neural networks
McCormac, J., Handa, A., Davison, A. J., & Leutenegger, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989538 · Cited by 684 · 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
Semantic Image Segmentation via Deep Parsing Network
Liu, Z., Li, X., Luo, P., Loy, C.-C., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.162 · Cited by 675 · see citing works
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 629 · 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
Semi Supervised Semantic Segmentation Using Generative Adversarial Network
Souly, N., Spampinato, C., & Shah, M. A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.606 · Cited by 540 · 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
Probabilistic data association for semantic SLAM
Bowman, S. L., Atanasov, N., Daniilidis, K., & Pappas, G. J. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989203 · Cited by 464 · see citing works
Learning Less is More - 6D Camera Localization via 3D Surface Regression
Brachmann, E., & Rother, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00489 · Cited by 399 · see citing works
Worldwide Pose Estimation Using 3D Point Clouds
Li, Y., Snavely, N., Huttenlocher, D. P., & Fua, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_2 · Cited by 369 · see citing works
Matterport3D: Learning from RGB-D Data in Indoor EnvironmentsPreprint
Chang, A., Dai, A., Funkhouser, T. A., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06158 · Cited by 332 · see citing works
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image SegmentationPreprint
Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02734 · Cited by 323 · see citing works
ROAD: 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
Schönberger, J. L., Pollefeys, M., Geiger, A., & Sattler, 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.2018.00721 · Cited by 271 · see citing works
Self-citationJoint 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
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Xie, J., Kiefel, M., Sun, M., & Geiger, A. (2016) · 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.2016.401 · Cited by 166 · see citing works
WildDash - Creating Hazard-Aware Benchmarks
Zendel, O., Honauer, K., Murschitz, M., Steininger, D., & Domínguez, G. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_25 · Cited by 164 · see citing works
Semantic structure from motion
Bao, S. Y., & Savarese, S. (2011) · doi:10.1109/cvpr.2011.5995462 · Cited by 154 · see citing works
Visual topometric localization
Badino, H., Huber, D., & Takeo Kanade, A. (2011) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2011.5940504 · Cited by 143 · see citing works
Semantic Match Consistency for Long-Term Visual Localization
Toft, C., Stenborg, E., Hammarstrand, L., Brynte, L., Pollefeys, M., Sattler, T., & Kahl, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_24 · Cited by 143 · see citing works
Self-citationJoint Optimization for Object Class Segmentation and Dense Stereo Reconstruction
Ladický, Ľ., Sturgess, P., Russell, C., Sengupta, S., Baştanlar, Y., Clocksin, W. F., & Torr, P. H. S. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0489-0 · Cited by 142 · see citing works
Lianos, K., Schönberger, J. L., Pollefeys, M., & Sattler, T. (2018) · European Conference on Computer Vision · doi:10.1007/978-3-030-01225-0_15 · Cited by 129 · see citing works
Self-citationEigen, D., & Fergus, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4734 · Cited by 126 · see citing works
Long-Term Visual Localization Using Semantically Segmented Images
Stenborg, E., Toft, C., & Hammarstrand, L. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8463150 · Cited by 124 · see citing works
Self-citationIncremental Adversarial Domain Adaptation for Continually Changing Environments
Wulfmeier, M., Bewley, A., & Posner, I. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460982 · Cited by 123 · see citing works
Semantic Stixels: Depth is not enough
Schneider, L., Cordts, M., Rehfeld, T., Pfeiffer, D., Enzweiler, M., Franke, U., Pollefeys, M., & Roth, S. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535373 · Cited by 90 · see citing works
Self-citationBlaha, M., Vogel, C., Richard, A., Wegner, J. D., Pock, T., & Schindler, K. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.346 · Cited by 88 · see citing works
Dense Semantic 3D Reconstruction
Häne, C., Zach, C., Cohen, A., & Pollefeys, M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2613051 · Cited by 84 · see citing works
Self-citationAddressing appearance change in outdoor robotics with adversarial domain adaptation
Wulfmeier, M., Bewley, A., & Posner, I. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205961 · Cited by 72 · see citing works
Semantic alignment of LiDAR data at city scale
Yu, F., Xiao, J., & Funkhouser, T. 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.2015.7298781 · Cited by 71 · see citing works
Multi-Label Semantic 3D Reconstruction Using Voxel Blocks
Cherabier, I., Häne, C., Oswald, M. R., & Pollefeys, M. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.68 · Cited by 56 · see citing works
Self-citationLong-Term 3D Localization and Pose from Semantic Labellings
Toft, C., Olsson, C., & Kahl, F. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.83 · Cited by 55 · see citing works
Self-citationSavinov, N., Häne, C., Ladický, Ľ., & Pollefeys, M. (2016) · 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.2016.589 · Cited by 51 · see citing works
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Cherabier, I., Schönberger, J. L., Oswald, M. R., Pollefeys, M., & Geiger, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_20 · Cited by 46 · see citing works
Self-citationSemantically guided location recognition for outdoors scenes
Mousavian, A., Košecká, J., & Lien, J. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139877 · Cited by 44 · see citing works
Indoor-Outdoor 3D Reconstruction Alignment
Cohen, A., Schönberger, J. L., Speciale, P., Sattler, T., Frahm, J., & Pollefeys, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_18 · Cited by 44 · see citing works
Self-citationVLASE: Vehicle Localization by Aggregating Semantic Edges
Yu, X., Chaturvedi, S., Feng, C., Taguchi, Y., Lee, T., Fernandes, C., & Ramalingam, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594358 · Cited by 44 · see citing works
Class Specific 3D Object Shape Priors Using Surface Normals
Häne, C., Savinov, N., & Pollefeys, M. (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.2014.89 · Cited by 39 · see citing works
Self-citationVisual Vocabulary with a Semantic Twist
Arandjelović, R., & Zisserman, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_12 · Cited by 36 · see citing works
Merging the Unmatchable: Stitching Visually Disconnected SfM Models
Cohen, A., Sattler, T., & Pollefeys, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.246 · Cited by 33 · see citing works
Self-citationImage to Image Translation for Domain AdaptationPreprint
Murez, Z., Kolouri, S., Kriegman, D., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00479 · Cited by 25 · see citing works
Semantically Guided Geo-location and Modeling in Urban Environments
Singh, G., & Košecká, J. (2016) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-25781-5_6 · Cited by 15 · see citing works
Revisiting Deep Structured Models for Pixel-Level Labeling with Gradient-Based Inference
Larsson, M., Arnab, A., Zheng, S., Torr, P. H. S., & Kahl, F. (2018) · SIAM Journal on Imaging Sciences · doi:10.1137/18m1167267 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- H. Badino, D. Huber, and T. Kanade. Visual topometric lo- calization. In Proc. IV, 2011. 3, 9
- A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei. Whats the point: Semantic segmentation with point super- vision. In Proc. ECCV. Springer, 2016. 2
- A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. De- Vito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Au- tomatic differentiation in pytorch. In NIPS Workshop, 2017. 5
- D. Pathak, P. Krahenbuhl, and T. Darrell. Constrained con- volutional neural networks for weakly supervised segmenta- tion. In Proc. ICCV, 2015. 2
- M. Wulfmeier, A. Bewley, and I. Posner. Incremental adver- sarial domain adaptation for continually changing environ- ments. In Proc. ICRA. IEEE, 2018. 2
Cited by
Sorted by citations
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
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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
Review the state-of-the-art technologies of semantic segmentation based on deep learning
Mo, Y., Wu, Y. X., Yang, X., Liu, F., & Liao, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.005 · Cited by 647 · see citing works
Sakaridis, C., Dai, D., & Gool, L. V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00747 · Cited by 295 · see citing works
Visual place recognition: A survey from deep learning perspective
Zhang, X., Wang, L., & Su, Y. L. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107760 · Cited by 262 · see citing works
Sakaridis, C., Dai, D., & Gool, L. V. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045882 · Cited by 200 · see citing works
Long-Term Visual Localization Revisited
Toft, C., Maddern, W., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., Šivic, J., Pajdla, T., et al. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3032010 · Cited by 196 · see citing works
Self-citationZaffar, M., Garg, S., Milford, M. J., Kooij, J. F. P., Flynn, D., McDonald-Maier, K. D., & Ehsan, S. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01469-5 · Cited by 150 · see citing works
Shen, W., Peng, Z., Wang, X., Wang, H., Cen, J., Jiang, D., Xie, L., Yang, X., & Tian, Q. C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3246102 · Cited by 153 · see citing works
Learning Accurate Dense Correspondences and When to Trust Them
Truong, P., Danelljan, M., Gool, L. V., & Timofte, R. (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/cvpr46437.2021.00566 · Cited by 130 · see citing works
RANSAC-Flow: Generic Two-Stage Image Alignment
Shen, X., Darmon, F., Efros, A. A., & Aubry, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58548-8_36 · Cited by 99 · see citing works
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
Truong, P., Danelljan, M., Timofte, R., & Gool, L. V. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3249225 · Cited by 96 · see citing works
Sun, L., Wang, K., Yang, K., & Xiang, K. (2019) · Artificial Intelligence and Machine Learning in Defense Applications · doi:10.1117/12.2532477 · Cited by 93 · see citing works
Zaffar, M., Ehsan, S., Milford, M. J., & McDonald-Maier, K. D. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969917 · Cited by 93 · see citing works
Larsson, M., Stenborg, E., Toft, C., Hammarstrand, L., Sattler, T., & Kahl, F. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00012 · Cited by 70 · see citing works
Self-citationRefign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Brüggemann, D., Sakaridis, C., Truong, P., & Gool, L. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00319 · Cited by 69 · see citing works
DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization
Hu, H., Qiao, Z., Cheng, M., Liu, Z., & Wang, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3043875 · Cited by 62 · see citing works
Warp Consistency for Unsupervised Learning of Dense Correspondences
Truong, P., Danelljan, M., Yu, F., & Gool, L. V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01018 · Cited by 49 · see citing works
How deep learning is empowering semantic segmentation
Sehar, U., & Naseem, M. L. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12821-3 · Cited by 51 · see citing works
Multi-scale Matching Networks for Semantic Correspondence
Zhao, D., Song, Z., Ji, Z., Zhao, G., Ge, W., & Yu, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00334 · Cited by 39 · see citing works
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description
Benbihi, A., Aravecchia, S., Geist, M., & Pradalier, C. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197529 · Cited by 38 · see citing works
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
Maheshwari, H., Liu, Y.-C., & Kira, Z. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00106 · Cited by 37 · see citing works
Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance
Yu, X., Zhao, Y. S., Gao, Y., Yuan, X., & Xiong, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01012 · Cited by 36 · see citing works
Ferrarini, B., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3148908 · Cited by 27 · see citing works
SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization
Pietrantoni, M., Humenberger, M., Sattler, T., & Csurka, G. (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/cvpr52729.2023.01476 · Cited by 25 · see citing works
Self-citationInteractive Learning of Intrinsic and Extrinsic Properties for All-Day Semantic Segmentation
Bi, Q., You, S., & Gevers, T. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3290469 · Cited by 23 · see citing works
2D Semantic Segmentation: Recent Developments and Future Directions
Guo, Y., Nie, G., Gao, W., & Liao, M. (2023) · Future Internet · doi:10.3390/fi15060205 · Cited by 18 · see citing works
Arcanjo, B., Ferrarini, B., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3140827 · Cited by 19 · see citing works
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation
Brüggemann, D., Sakaridis, C., Brödermann, T., & Gool, L. V. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01045 · Cited by 21 · see citing works
Nie, J., Feng, J.-M., Xue, D., Pan, F., Liu, W., da Hu, J., & Cheng, S. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3320489 · Cited by 16 · see citing works
ROVER: A Multiseason Dataset for Visual SLAM
Schmidt, F., Daubermann, J., Mitschke, M., Blessing, C., Meyer, S., Enzweiler, M., & Valada, A. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3577026 · Cited by 16 · see citing works
Hu, H., Yang, B., Qiao, Z., Liu, S., Zhu, J., Liu, Z., Ding, W., Zhao, D. X., & Wang, H. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341917 · Cited by 15 · see citing works
Pole-like Objects Mapping and Long-Term Robot Localization in Dynamic Urban Scenarios
Wang, Z., Li, S., Cao, M. W., Chen, H., & Liu, Y. (2021) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio54168.2021.9739599 · Cited by 13 · see citing works
CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition
Ming, Y., Yang, X., Zhang, G., & Calway, A. D. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981113 · Cited by 11 · see citing works
Learning invariant semantic representation for long-term robust visual localization
Wu, J., Shi, Q., Lu, Q., Liu, X., Zhu, X., & Lin, Z. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104793 · Cited by 11 · see citing works
Improving Visual Place Recognition Performance by Maximising Complementarity
Waheed, M., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3088779 · Cited by 9 · see citing works
Forest: A Lightweight Semantic Image Descriptor for Robust Visual Place Recognition
Hou, P., Chen, J., Nie, J., Liu, Y., & Zhao, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3219030 · Cited by 8 · see citing works
A Review On Thermal Infrared Semantic Distribution for Nightfall Drive
Bandi, M., & Reeja, S. R. (2022) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict54557.2022.9917651 · Cited by 8 · see citing works
Hierarchical Visual Place Recognition Based on Semantic-Aggregation
Chen, B., Song, X., Shen, H., & Lü, T. (2021) · Applied Sciences · doi:10.3390/app11209540 · Cited by 8 · see citing works
Evaluation of Global Descriptor Methods for Appearance-Based Visual Place Recognition
Li, K., Ma, Y., Wang, X., Ji, L., & Geng, N. (2023) · Journal of Robotics · doi:10.1155/2023/9150357 · Cited by 7 · see citing works
LIFE: Lighting Invariant Flow EstimationPreprint
Huang, Z., Pan, X., Xu, R., Xu, Y. L., Cheung, K. C., Zhang, G., & Li, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.03097 · Cited by 7 · see citing works
Tan, Y., Ji, P., Zhang, Y., Ge, F., & Zhu, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109425 · Cited by 7 · see citing works
Double-Domain Adaptation Semantics for Retrieval-Based Long-Term Visual Localization
Ge, F., Zhang, Y., Wang, L., Coleman, S., & Kerr, D. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3345138 · Cited by 6 · see citing works
AEGIS-Net: Attention-Guided Multi-Level Feature Aggregation for Indoor Place Recognition
Ming, Y., Ma, J., Yang, X., Dai, W., Peng, Y. K., & Kong, W. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447578 · Cited by 6 · see citing works
Highly-Efficient Binary Neural Networks for Visual Place Recognition
Ferrarini, B., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981978 · Cited by 6 · see citing works
Autoencoder and Partially Impossible Reconstruction Losses
Cruz, S., Taetz, B., Stifter, T., & Stricker, D. (2022) · Sensors · doi:10.3390/s22134862 · Cited by 4 · see citing works
Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
Cruz, S., Taetz, B., Stifter, T., & Stricker, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00150 · Cited by 4 · see citing works
Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object ImagesPreprint
Guo, J., Yang, Z., Xi, L., & Zhang, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04237 · Cited by 4 · see citing works
An Improved Monocular SLAM System by Combining Geometric Features With Semantic Probability
Chen, J., Hou, P., Cao, Z., Xu, T. L., & Zhao, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3575502 · Cited by 3 · see citing works
Understanding and Exploiting Dependent Variables with Deep Metric Learning
O’Mahony, N., Campbell, S., Carvalho, A., Krpalkova, L., Velasco-Hernandez, G., Riordan, D., & Walsh, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55180-3_8 · Cited by 3 · see citing works
Scene-Unified Image Translation For Visual Localization
Han, S., Gao, W., Wan, Y., & Wu, Y. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190885 · Cited by 3 · see citing works
Waheed, M., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981722 · Cited by 3 · see citing works
Domain Adaptation of Learned Features for Visual LocalizationPreprint
Baik, S., Kim, H. J., Shen, T., Ilg, E., Lee, K. M., & Sweeney, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09310 · Cited by 3 · see citing works
MDAG-Net: Multidomain Association-Guided Network for Image-Based Long-Term Visual Localization
Ge, F., Zhang, Y., Wang, L., Tan, Y., Coleman, S., & Kerr, D. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2024.3525180 · Cited by 3 · see citing works
Binary Graph Descriptor for Robust Relocalization on Heterogeneous Data
Wang, X., Christie, M., & Marchand, É. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3142854 · Cited by 2 · see citing works
SGDA: A Saliency-Guided Domain Adaptation Network for Nighttime Semantic Segmentation
Duan, Y., Tu, J., & Chen, C. (2023) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps58381.2023.10128083 · Cited by 2 · see citing works
Li, K., Wang, X., Shi, L., & Geng, N. (2023) · IEEE Access · doi:10.1109/access.2023.3246803 · Cited by 2 · see citing works
Visual Place Recognition Using Region of Interest Extraction with Deep Learning Based Approach
Sasikumar, P., & Sathiamoorthy, S. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-2541-2_28 · Cited by 2 · see citing works
Wu, X., Chen, Y., Pradalier, C., & Vela, P. A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561964 · Cited by 1 · see citing works
Learning a Robust Hybrid Descriptor for Robot Visual Localization
Shi, Q., Wu, J., Lin, Z., & Qin, N. (2022) · Journal of Robotics · doi:10.1155/2022/9354909 · Cited by 1 · see citing works
Nie, J., Feng, J.-M., Xue, D., Pan, F., Liu, W., da Hu, J., & Cheng, S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956385 · Cited by 1 · see citing works
Training-Free Attentive-Patch Selection for Visual Place Recognition
Zhang, D., Wu, M., & Lam, S.-K. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342347 · Cited by 1 · see citing works
An Appearance-Semantic Descriptor with Coarse-to-Fine Matching for Robust VPR
Chen, J., Li, W., Hou, P., Yang, Z., & Zhao, H. (2024) · Sensors · doi:10.3390/s24072203 · Cited by 1 · see citing works
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
Spencer, J., Bowden, R., & Hadfield, 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/cvpr42600.2020.00649
Dai, D., Tan, R. T., Patel, V. M., Matas, J., Schiele, B., & Gool, L. V. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01464-w
CLSD: Continual Learning for Lane Line Segmentation Across Domains
Wu, Y. X., Liao, Y., Jiang, W., Zhao, J., Liu, F., & Mo, Y. (2022) · IEEE International Conference on Intelligent Transportation Engineering (ICITE) · doi:10.1109/icite56321.2022.10101413
Larsen, M. K., Pizarro, O. R., & Ludvigsen, M. (2023) · IEEE Underwater Technology (UT) · doi:10.1109/ut49729.2023.10103451
Towards Modeling 3D Dense Shape Correspondence from Category-Specific Multi-View Images
Yang, Z., & Zhang, Q. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10223104
A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition
Waheed, M., Waheed, S., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341876
CNN Models for Accurate Pose LocalizationPreprint
Elouni, A., Royer, E., & Château, T. (2024) · Research Square · doi:10.21203/rs.3.rs-4283976/v1
Chen, C., Liu, H., Liu, C., Chang, F., Li, T., & Hao, P. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131286
- Authors
- Mans Larsson, M., Stenborg, E., Hammarstrand, L., Pollefeys, M., Sattler, T., & Kahl, F.
- Affiliations
- Chalmers
- Chalmers University of Technology (SE)
- ETH Zurich (CH)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 9524-9534
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2921320302
- Cited by
- 71
- References
- 82
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 17 · 2022: 17 · 2021: 13 · 2020: 12 · 2019: 3
- FWCI
- 3.67
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Stiftelsen för Strategisk Forskning
- Vetenskapsrådet (2016-04445)
- 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
- semantic segmentation, weather robustness, 3D model reconstruction, label consistency, dataset construction
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