Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
Abstract
Our abilities in scene understanding, which allow us to perceive the 3D structure of our surroundings and intuitively recognise the objects we see, are things that we largely take for granted, but for robots, the task of understanding large scenes quickly remains extremely challenging. Recently, scene understanding approaches based on 3D reconstruction and semantic segmentation have become popular, but existing methods either do not scale, fail outdoors, provide only sparse reconstructions or are rather slow. In this paper, we build on a recent hash-based technique for large-scale fusion and an efficient mean-field inference algorithm for densely-connected CRFs to present what to our knowledge is the first system that can perform dense, large-scale, outdoor semantic reconstruction of a scene in (near) real time. We also present a `semantic fusion' approach that allows us to handle dynamic objects more effectively than previous approaches. We demonstrate the effectiveness of our approach on the KITTI dataset, and provide qualitative and quantitative results showing high-quality dense reconstruction and labelling of a number of scenes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Vineet et al. proposed a promising online system using stereo cameras and a dense map representation.
“Recently, a promising online system was proposed by Vineet et al. [251] using stereo cameras and a dense map representation.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [251]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988 · see citing works
(2009) · Cited by 6,153 · see citing works
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346 · see citing works
Self-citationKinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008 · see citing works
Self-citationMonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959 · see citing works
LSD-SLAM: Large-scale direct monocular SLAM
Engel, J., Schöps, T., & Cremers, D. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10605-2_54 · Cited by 3,851 · see citing works
A volumetric method for building complex models from range images
Curless, B., & Levoy, M. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237269 · Cited by 3,124 · see citing works
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Krähenbühl, P., & Koltun, V. (2012) · arXiv · doi:10.48550/arxiv.1210.5644 · Cited by 2,908 · see citing works
SVO: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906584 · Cited by 2,205 · see citing works
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · see citing works
Autonomous driving in urban environments: Boss and the Urban Challenge
Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M. N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20255 · Cited by 1,555 · see citing works
Towards fully autonomous driving: Systems and algorithms
Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Kolter, J. Z., Langer, D., Pink, O., Pratt, V., et al. (2011) · 2011 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2011.5940562 · Cited by 1,374 · see citing works
Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S. M., & Szeliski, R. (2011) · Communications of the ACM · doi:10.1145/2001269.2001293 · Cited by 1,077 · see citing works
Agarwal, S., Snavely, N., Simon, I., Seitz, S. M., & Szeliski, R. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459148 · Cited by 1,066 · see citing works
Semantic texton forests for image categorization and segmentation
Shotton, J., Johnson, M., & Cipolla, R. (2008) · 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.2008.4587503 · Cited by 1,027 · see citing works
Real-time 3D reconstruction at scale using voxel hashing
Nießner, M., Zollhöfer, M., Izadi, S., & Stamminger, M. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508374 · Cited by 1,001 · see citing works
Self-citationEfficient Large-Scale Stereo Matching
Geiger, A., Roser, M., & Urtasun, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19315-6_3 · Cited by 787 · see citing works
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera
Huang, A. S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., & Roy, N. (2016) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-29363-9_14 · Cited by 607 · see citing works
Contextual classification with functional Max-Margin Markov Networks
Munoz, D., Bagnell, J. A., Vandapel, N., & Hebert, M. (2009) · 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.2009.5206590 · Cited by 346 · see citing works
Self-supervised Monocular Road Detection in Desert Terrain
Dahlkamp, H., Kaehler, A., Stavens, D., Thrun, S., & Bradski, G. R. (2006) · Robotics: Science and Systems · doi:10.15607/rss.2006.ii.005 · Cited by 330 · see citing works
Semantic Labeling of 3D Point Clouds for Indoor Scenes
Koppula, H. S., Anand, A., Joachims, T., & Saxena, A. (2011) · neural information processing systems · Cited by 322 · see citing works
Dense 3D semantic mapping of indoor scenes from RGB-D images
Hermans, A., Floros, G., & Leibe, B. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907236 · Cited by 258 · see citing works
Scalable real-time volumetric surface reconstruction
Chen, J., Bautembach, D., & Izadi, S. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461940 · Cited by 247 · 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
Joint 3D Scene Reconstruction and Class Segmentation
Häne, C., Zach, C., Cohen, A., Angst, R., & Pollefeys, M. (2013) · 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.2013.20 · Cited by 213 · see citing works
Real-Time Dense Geometry from a Handheld Camera
Stühmer, J., Gumhold, S., & Cremers, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15986-2_2 · Cited by 205 · see citing works
Autonomous Driving in Urban Environments: Boss and the Urban Challenge
Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M., Dolan, J. M., Duggins, D., Galatali, T., Geyer, C., et al. (2009) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-03991-1_1 · Cited by 198 · see citing works
fastHOG – a real-time GPU implementation of HOG
Prisacariu, V. A., & Reid, I. D. (2011) · Cited by 152 · see citing works
Self-citationUrban 3D semantic modelling using stereo vision
Sengupta, S., Greveson, E., Shahrokni, A., & Torr, P. H. S. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630632 · Cited by 143 · see citing works
Self-citationA Depth-Based Head-Mounted Visual Display to Aid Navigation in Partially Sighted Individuals
Hicks, S. L., Wilson, I., Muhammed, L., Worsfold, J., Downes, S. M., & Christopher Kennard, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067695 · Cited by 110 · see citing works
Mesh Based Semantic Modelling for Indoor and Outdoor Scenes
Valentin, J. P. C., Sengupta, S., Warrell, J., Shahrokni, A., & Torr, P. H. S. (2013) · 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.2013.269 · Cited by 94 · see citing works
Self-citationJoint 2D-3D temporally consistent semantic segmentation of street scenes
Floros, G., & Leibe, 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.2012.6248007 · Cited by 93 · see citing works
Automatic dense visual semantic mapping from street-level imagery
Sengupta, S., Sturgess, P., Ladický, Ľ., & Torr, P. H. S. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385958 · Cited by 91 · see citing works
Self-citationAssociative Hierarchical Random Fields
Ladický, Ľ., Russell, C., Kohli, P., & Torr, P. H. S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.165 · Cited by 84 · see citing works
Self-citationCity-Scale Change Detection in Cadastral 3D Models Using Images
Taneja, A., Ballan, L., & Pollefeys, M. (2013) · 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.2013.22 · Cited by 71 · see citing works
Efficient 3-D scene analysis from streaming data
Hu, H., Munoz, D., Bagnell, J. A., & Hebert, M. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630888 · Cited by 47 · see citing works
Filter-Based Mean-Field Inference for Random Fields with Higher-Order Terms and Product Label-Spaces
Vineet, V., Warrell, J., & Torr, P. H. S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_3 · Cited by 43 · see citing works
Self-citationMean field approach for tracking similar objects
Medrano, C., Herrero, E., del Rincón, J. M., & Orrite, C. (2009) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.03.007 · Cited by 9 · see citing works
Flagged first, then by citations
5 of the 44 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596 · see citing works
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094 · see citing works
CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction
Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.695 · Cited by 800 · 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
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
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 · see citing works
SuMa++: Efficient LiDAR-based Semantic SLAM
Chen, X., Milioto, A., Palazzolo, E., Giguère, P., Behley, J., & Stachniss, C. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967704 · Cited by 570 · 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
OpenScene: 3D Scene Understanding with Open Vocabularies
Peng, S., Genova, K., Jiang, C., Tagliasacchi, A., Pollefeys, M., & Funkhouser, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00085 · Cited by 395 · see citing works
3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation
Dai, A., & Nießner, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_28 · Cited by 347 · see citing works
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · doi:10.1561/9781680836899 · Cited by 243 · see citing works
Pham, Q.-H., Nguyen, T., Hua, B., Roig, G., & Yeung, S.-K. (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.2019.00903 · Cited by 227 · see citing works
Meaningful maps – Object-oriented semantic mapping
Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M. J., & Reid, I. D. (2017) · QUT ePrints (Queensland University of Technology) · Cited by 219 · see citing works
An Overview on Visual SLAM: From Tradition to Semantic
Chen, W.-F., Shang, G., Ji, A.-H., Zhou, C., Wang, X., Xu, C., Li, Z., & Hu, K. (2022) · Remote Sensing · doi:10.3390/rs14133010 · Cited by 217 · see citing works
Semantic Stereo for Incidental Satellite Images
Bosch, M., Foster, K., Christie, G. A., Wang, S. H., Hager, G. D., & Brown, M. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00167 · Cited by 205 · see citing works
Motion removal for reliable RGB-D SLAM in dynamic environments
Sun, Y., Liu, M., & Meng, M. Q. ‐. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.07.002 · Cited by 200 · see citing works
A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving
Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S. K., Jägersand, M., & Zhang, H. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00101 · Cited by 187 · see citing works
Monocular Semantic Occupancy Grid Mapping With Convolutional Variational Encoder–Decoder Networks
Lu, C., van de Molengraft, R., & Dubbelman, G. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2891028 · Cited by 169 · see citing works
Virtual Multi-view Fusion for 3D Semantic Segmentation
Kundu, A., Yin, X., Fathi, A., Ross, D. A., Brewington, B. E., Funkhouser, T. A., & Pantofaru, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_31 · Cited by 169 · see citing works
Recent trends in social aware robot navigation: A survey
Charalampous, K., Kostavelis, I., & Gasteratos, A. C. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.03.002 · Cited by 168 · see citing works
Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges
Siam, M., Elkerdawy, S., Jagersand, M., & Yogamani, S. (2017) · 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317714 · Cited by 178 · see citing works
Stereo Vision-Based Semantic 3D Object and Ego-Motion Tracking for Autonomous Driving
Li, P., Qin, T., & Shen, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_40 · Cited by 156 · see citing works
Active Autonomous Aerial Exploration for Ground Robot Path Planning
Delmerico, J., Mueggler, E., Nitsch, J., & Scaramuzza, D. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2651163 · Cited by 149 · 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
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations
Tschernezki, V., Laina, I., Larlus, D., & Vedaldi, A. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00056 · Cited by 142 · see citing works
Robust Dense Mapping for Large-Scale Dynamic Environments
Bârsan, I. A., Liu, P., Pollefeys, M., & Geiger, A. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8462974 · Cited by 136 · see citing works
Choudhary, S., Carlone, L., Nieto-Granda, C., Rogers, J. G., Christensen, H. I., & Dellaert, F. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917732640 · Cited by 134 · see citing works
Path Planning Technique for Mobile Robots: A Review
Yang, L., Li, P., Qian, S., He, Q., Miao, J., Liu, M., Hu, Y., & Memetimin, E. (2023) · Machines · doi:10.3390/machines11100980 · Cited by 139 · see citing works
RS-SLAM: A Robust Semantic SLAM in Dynamic Environments Based on RGB-D Sensor
Ran, T., Yuan, L., Zhang, J., Tang, D., & He, L. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3099511 · Cited by 110 · see citing works
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 114 · see citing works
Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning
Milz, S., Arbeiter, G., Witt, C., Abdallah, B., & Yogamani, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00062 · Cited by 107 · see citing works
Convolutional gated recurrent networks for video segmentation
Siam, M., Valipour, S., Jägersand, M., & Ray, N. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296851 · Cited by 107 · see citing works
Building and optimization of 3D semantic map based on Lidar and camera fusion
Li, J., Zhang, X., Li, J., Liu, Y., & Wang, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.004 · Cited by 107 · see citing works
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
Wang, J., Rünz, M., & Agapito, L. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00143 · Cited by 94 · see citing works
When 2.5D is not enough: Simultaneous reconstruction, segmentation and recognition on dense SLAM
Tateno, K., Tombari, F., & Navab, N. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487378 · Cited by 92 · see citing works
Blaha, 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
Semantic 3D occupancy mapping through efficient high order CRFs
Yang, S., Huang, Y., & Scherer, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202212 · Cited by 85 · see citing works
Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey
Sualeh, M., & Kim, G.-W. (2018) · International Journal of Control, Automation and Systems · doi:10.1007/s12555-018-0130-x · Cited by 78 · see citing works
Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
Gan, L., Zhang, R., Grizzle, J. W., Eustice, R. M., & Ghaffari, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965390 · Cited by 83 · see citing works
DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes
Cheng, J., Wang, Z., Zhou, H., Li, L., & Yao, J. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9040202 · Cited by 80 · see citing works
Semantic Monocular SLAM for Highly Dynamic Environments
Brasch, N., Božič, A., Lallemand, J., & Tombari, F. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593828 · Cited by 78 · see citing works
Semantic segmentation of 3D textured meshes for urban scene analysis
Rouhani, M., Lafarge, F., & Alliez, P. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.12.001 · Cited by 77 · see citing works
Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age.Preprint
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · arXiv (Cornell University) · Cited by 78 · see citing works
Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks
Langer, F., Milioto, A., Haag, A., Behley, J., & Stachniss, C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341508 · Cited by 76 · see citing works
Learning 3D Semantic Segmentation with only 2D Image Supervision
Genova, K., Yin, X., Kundu, A., Pantofaru, C., Cole, F. H., Sud, A., Brewington, B. E., Shucker, B., & Funkhouser, T. A. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00046 · Cited by 76 · see citing works
Real-Time Progressive 3D Semantic Segmentation for Indoor Scenes
Pham, Q.-H., Hua, B., Nguyen, T. M., & Yeung, S.-K. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00121 · Cited by 74 · see citing works
Depth Based Semantic Scene Completion With Position Importance Aware Loss
Li, J., Liu, Y., Yuan, X., Zhao, C., Siegwart, R., Reid, I. D., & Cadena, C. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2953639 · Cited by 72 · see citing works
Chen, Y., Zhang, B., Zhou, J. C., & Wang, K. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105579 · Cited by 72 · 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
Massiceti, D., Hicks, S. L., & van Rheede, J. J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199389 · Cited by 54 · see citing works
A Computationally Efficient Semantic SLAM Solution for Dynamic Scenes
Wang, Z., Zhang, Q., Li, J., Zhang, S., & Liu, J. (2019) · Remote Sensing · doi:10.3390/rs11111363 · Cited by 49 · see citing works
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D ScenesPreprint
Vora, S., Radwan, N., Greff, K., Meyer, H., Genova, K., Sajjadi, M. S. M., Pot, E., Tagliasacchi, A., & Duckworth, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13260 · Cited by 50 · see citing works
Scene flow propagation for semantic mapping and object discovery in dynamic street scenes
Kochanov, D., Ošep, A., Stückler, J., & Leibe, B. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759285 · Cited by 49 · see citing works
Real-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction
Piazza, E., Romanoni, A., & Matteucci, M. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800104 · Cited by 51 · see citing works
Visual-Inertial-Semantic Scene Representation for 3D Object Detection
Dong, J., Fei, X., & Soatto, S. (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.2017.380 · Cited by 47 · see citing works
Multimodal sensor-based semantic 3D mapping for a large-scale environment
Jeong, J., Yoon, T. S., & Park, J. B. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.051 · Cited by 47 · see citing works
Zhao, C., Sun, L., & Stolkin, R. (2017) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2017.8023499 · Cited by 45 · see citing works
The Raincouver Scene Parsing Benchmark for Self-Driving in Adverse Weather and at Night
Tung, F., Chen, J., Meng, L., & Little, J. J. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2723926 · Cited by 45 · see citing works
Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes
Mustafa, A., & Hilton, 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.2017.592 · Cited by 44 · see citing works
Stereo Vision Based Sensory Substitution for the Visually Impaired
Caraiman, S., Zvorişteanu, O., Burlacu, A., & Herghelegiu, P. (2019) · Sensors · doi:10.3390/s19122771 · Cited by 44 · see citing works
Playing Doom with SLAM-Augmented Deep Reinforcement LearningPreprint
Bhatti, S., Desmaison, A., Mikšík, O., Nardelli, N., Siddharth, N., & Torr, P. H. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00380 · Cited by 48 · see citing works
Self-citationZhang, X., Kundu, A., Funkhouser, T. A., Guibas, L. J., Su, H., & Genova, K. (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/cvpr52729.2023.00800 · Cited by 41 · see citing works
Semantic Implicit Neural Scene Representations With Semi-Supervised Training
Kohli, A., Sitzmann, V., & Wetzstein, G. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00052 · Cited by 39 · see citing works
Dynamic body VSLAM with semantic constraints
Reddy, N. D., Singhal, P., Chari, V., & Krishna, K. M. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353626 · Cited by 39 · see citing works
ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes
Lim, H., Nunes, L., Mersch, B., Chen, X., Behley, J., Myung, H., & Stachniss, C. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.067 · Cited by 38 · see citing works
Towards a Meaningful 3D Map Using a 3D Lidar and a Camera
Jeong, J., Yoon, T. S., & Park, J. B. (2018) · Sensors · doi:10.3390/s18082571 · Cited by 38 · see citing works
Overview and Empirical Analysis of ISP Parameter Tuning for Visual Perception in Autonomous Driving
Yahiaoui, L., Horgan, J., Deegan, B. M., Yogamani, S. K., Eising, C., & Denny, P. E. (2019) · Journal of Imaging · doi:10.3390/jimaging5100078 · Cited by 36 · see citing works
Exploring Shared Perception and Control in Cooperative Vehicle-Intersection Systems: A Review
Bejarbaneh, E. Y., Du, H., & Naghdy, F. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3432634 · Cited by 37 · see citing works
Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation
Nakajima, Y., Tateno, K., Tombari, F., & Saitô, H. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593993 · Cited by 37 · see citing works
Efficient Object-Oriented Semantic Mapping With Object Detector
Nakajima, Y., & Saitô, H. (2018) · IEEE Access · doi:10.1109/access.2018.2887022 · Cited by 36 · see citing works
Bujanca, M., Gafton, P., Sajad Saeedi, G., Nisbet, A. P., Bodin, B., OaBoyle Michael, F. P., Davison, A. J., J., K. P. H., Riley, G. D., Lennox, B., et al. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794369 · Cited by 34 · see citing works
Semantically Informed Multiview Surface Refinement
Blaha, M., Rothermel, M., Oswald, M. R., Sattler, T., Richard, A., Wegner, J. D., Pollefeys, M., & Schindler, K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.412 · Cited by 11 · see citing works
Semantic RGB-D SLAM for Rescue Robot Navigation
Deng, W., Huang, K., Chen, X., Zhou, Z., Shi, C., Guo, R., & Zhang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3031867 · Cited by 32 · see citing works
Survey of connected automated vehicle perception mode: from autonomy to interaction
Shangguan, W., Du, Y., Guo, C. L., Liu, D., & Shu, W. W. (2018) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5239 · Cited by 32 · see citing works
Crowd-sourced Semantic Edge Mapping for Autonomous Vehicles
Herb, M., Weiherer, T., Navab, N., & Tombari, F. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968020 · Cited by 31 · see citing works
LaTeRF: Label and Text Driven Object Radiance Fields
Mirzaei, A., Kant, Y., Kelly, J. W., & Gilitschenski, I. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_2 · Cited by 29 · see citing works
SmartCaveDrone: 3D cave mapping using UAVs as robotic co-archaeologists
Zhang, G., Shang, B., Chen, Y., & Moyes, H. (2017) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2017.7991499 · Cited by 29 · see citing works
Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization
Armagan, A., Hirzer, M., Roth, P. M., & Lepetit, V. (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.2017.488 · Cited by 27 · see citing works
Dense RGB-D Semantic Mapping with Pixel-Voxel Neural Network
Zhao, C., Sun, L., Purkait, P., Duckett, T., & Stolkin, R. (2018) · Sensors · doi:10.3390/s18093099 · Cited by 27 · see citing works
Semantically-Aware Aerial Reconstruction from Multi-modal Data
Cabezas, R., Straub, J., & Fisher, J. S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.249 · Cited by 25 · see citing works
Semantic mapping techniques for indoor mobile robots: Review and prospect
Xu, S., Liang, X., & Huaidong, Z. (2024) · Measurement and Control · doi:10.1177/00202940241259903 · Cited by 23 · see citing works
Meaningful maps with object-oriented semantic mapping
Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M. J., & Reid, I. D. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206392 · Cited by 23 · see citing works
Diffuser: Multi-View 2D-to-3D Label Diffusion for Semantic Scene Segmentation
Mascaro, R., Teixeira, L., & Chli, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561801 · Cited by 23 · see citing works
Dynamic dense CRF inference for video segmentation and semantic SLAM
You, M., Luo, C., Zhou, H., & Zhu, S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109023 · Cited by 23 · see citing works
Ran, T., Yuan, L., Zhang, J., He, L., Huang, R., & Mei, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3074954 · Cited by 23 · see citing works
NEURAL NETWORKS FOR THE CLASSIFICATION OF BUILDING USE FROM STREET-VIEW IMAGERY
Laupheimer, D., Tutzauer, P., Norbert Haala, A., & Spicker, M. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-177-2018 · Cited by 22 · see citing works
Large-scale 3D Semantic Mapping Using Stereo Vision
Yang, Y. M., Qiu, F., Li, H., Zhang, L., Wang, M., & Fu, M. (2018) · Machine Intelligence Research · doi:10.1007/s11633-018-1118-y · Cited by 21 · see citing works
Visual-Inertial Object Detection and Mapping
Fei, X., & Soatto, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_19 · Cited by 21 · see citing works
Incremental Class Discovery for Semantic Segmentation With RGBD Sensing
Nakajima, Y., Kang, B., Saitô, H., & Kitani, K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00106 · Cited by 21 · see citing works
Gaussian Processes Semantic Map RepresentationPreprint
Jadidi, M. G., Gan, L., Parkison, S. A., Li, J., & Eustice, R. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01532 · Cited by 21 · see citing works
Zobeidi, E., Koppel, A., & Atanasov, N. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3168733 · Cited by 19 · see citing works
Lightweight Semantic-Aided Localization With Spinning LiDAR Sensor
Ren, Y., Liu, B., Cheng, R., & Agia, C. (2021) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2021.3099022 · Cited by 18 · see citing works
Semantic-Direct Visual Odometry
Bao, Y., Yang, Z., Pan, Y., & Huan, R. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3176799 · Cited by 18 · see citing works
A Review of Common Techniques for Visual Simultaneous Localization and Mapping
Dai, Y., Wu, J., & Wang, D. (2023) · Journal of Robotics · doi:10.1155/2023/8872822 · Cited by 18 · see citing works
NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping
Yu, X., Liu, Y., Mao, S., Zhou, S., Xiong, R., Liao, Y., & Wang, Y. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3300281 · Cited by 18 · see citing works
Brandão, M., Hashimoto, K., & Takanishi, A. (2016) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2016.7803311 · Cited by 18 · see citing works
Looking forward: A semantic mapping system for scouting with micro-aerial vehicles
Maturana, D., Arora, S., & Scherer, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206585 · Cited by 18 · see citing works
Octree map based on sparse point cloud and heuristic probability distribution for labeled images
Berrío, J. S., Zhou, W., Ward, J., Worrall, S., & Nebot, E. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594024 · Cited by 18 · see citing works
- Authors
- Vineet, V., Miksik, O., Lidegaard, M., Niebner, M., Golodetz, S., Prisacariu, V. A., Kahler, O., Murray, D. W., Izadi, S., Peerez, P., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Microsoft (United States) (US)
- Technicolor (France) (FR)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 75-82
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2167687475
- Cited by
- 200
- References
- 44
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 14 · 2022: 15 · 2021: 19 · 2020: 24 · 2019: 28 · 2018: 34 · 2017: 29 · 2016: 15 · 2015: 4
- FWCI
- 11.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/J014990/1, EP/I001107/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- dense 3D reconstruction, mean-field inference, conditional random fields, outdoor scene understanding, real-time reconstruction, semantic segmentation, KITTI dataset
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
