VirtualWorlds as Proxy for Multi-object Tracking Analysis

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

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

A kernel two-sample test

(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603 · see citing works

Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

Bernardin, K., & Stiefelhagen, R. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/246309 · Cited by 2,918 · see citing works

Edge Boxes: Locating Object Proposals from Edges

Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656 · see citing works

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876 · see citing works

A Naturalistic Open Source Movie for Optical Flow Evaluation

Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842 · see citing works

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures

Bergstra, J., Yamins, D. L. K., & Cox, D. (2013) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1,817 · see citing works

Global data association for multi-object tracking using network flows

Zhang, L., Li, Y., & Nevatia, 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.2008.4587584 · Cited by 945 · see citing works

Globally-optimal greedy algorithms for tracking a variable number of objects

Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995604 · Cited by 790 · see citing works

Learning to Track: Online Multi-object Tracking by Decision Making

Yu, X., Alahi, A., & Savarese, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.534 · Cited by 684 · see citing works

Simulation as an engine of physical scene understanding

Battaglia, P. W., Hamrick, J. B., & Tenenbaum, J. B. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1306572110 · Cited by 681 · see citing works

Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera

Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Gool, L. V. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.232 · Cited by 579 · see citing works

Continuous Energy Minimization for Multitarget Tracking

Milan, A., Roth, S., & Schindler, K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.103 · Cited by 556 · 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

Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor

Choi, W. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.347 · Cited by 409 · see citing works

3D Traffic Scene Understanding From Movable Platforms

Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.185 · Cited by 397 · 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

Motion Capture of Hands in Action Using Discriminative Salient Points

Ballan, L., Taneja, A., Gall, J., Gool, L. V., & Pollefeys, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_46 · Cited by 285 · see citing works

Teaching 3D geometry to deformable part models

Pepik, B., Stark, M. M., Gehler, P., & 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.2012.6248075 · Cited by 209 · see citing works

Inferring 3D structure with a statistical image-based shape model

Grauman, Shakhnarovich, & Darrell (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238408 · Cited by 197 · 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

Back to the Future: Learning Shape Models from 3D CAD Data

Stark, M. M., Goesele, M., & Schiele, B. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.106 · Cited by 136 · 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

Picture: A probabilistic programming language for scene perception

Kulkarni, T. D., Kohli, P., Tenenbaum, J. B., & Mansinghka, V. K. (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.2015.7299068 · Cited by 133 · see citing works

OVVV: Using Virtual Worlds to Design and Evaluate Surveillance Systems

Taylor, G. R., Chosak, A. J., & Brewer, P. C. (2007) · 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.2007.383518 · Cited by 117 · see citing works

Differences between stereo and motion behaviour on synthetic and real-world stereo sequences

Vaudrey, T., Rabe, C., Klette, R., & Milburn, J. (2008) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2008.4762133 · Cited by 116 · see citing works

Data-Driven Scene Understanding from 3D Models

Satkin, S., Lin, J., & Hebert, M. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.128 · Cited by 80 · see citing works

Evaluation of image features using a photorealistic virtual world

Kaneva, B., Torralba, A. B., & Freeman, W. T. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126508 · Cited by 73 · see citing works

Multiple Object Tracking: A Review.Preprint

Luo, W., Zhao, X., & Kim, T. (2014) · arXiv (Cornell University) · Cited by 60 · see citing works

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics ProgramsPreprint

Mansinghka, V. K., Kulkarni, T. D., Perov, Y., & Tenenbaum, J. B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.0060 · Cited by 55 · see citing works

Learning Optimal Parameters For Multi-target Tracking

Wang, S., & Fowlkes, C. C. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.4 · Cited by 33 · see citing works

Model-based validation approaches and matching techniques for automotive vision based pedestrian detection

Broggi, A., Fascioli, A., Grisleri, P., Graf, T., & Meinecke, M.-M. (2006) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvpr.2005.495 · Cited by 32 · see citing works

Online Domain Adaptation for Multi-Object Tracking

Gaidon, A., & Vig, E. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.3 · Cited by 25 · see citing works

Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians

Collins, R. T., & Carr, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_20 · Cited by 25 · 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

Online, Real-Time Tracking Using a Category-to-Individual Detector

Hall, D. C., & Perona, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_24 · Cited by 12 · see citing works

Unsupervised Monocular Depth Estimation with Left-Right Consistency

Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270 · 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

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

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

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

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

Multiple object tracking: A literature review

Luo, W., Xing, J., Milan, A., Zhang, X.-Q., Liu, W., Zhao, X., & Kim, T.-K. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2020.103448 · Cited by 1,095 · 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

Learning from Synthetic Humans

Varol, G., Romero, J., Martín, X., Mahmood, N., Black, M. J., Laptev, I., & Schmid, C. (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.2017.492 · Cited by 915 · see citing works

Sparsity Invariant CNNs

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

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

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

A Survey on 3D Object Detection Methods for Autonomous Driving Applications

Arnold, E. H., Al-Jarrah, O. Y., Dianati, M., Fallah, S., Oxtoby, D., & Mouzakitis, A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2892405 · Cited by 720 · 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

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

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

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

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

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

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

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

TartanAir: A Dataset to Push the Limits of Visual SLAM

Wang, W., Zhu, D., Wang, X., Hu, Y., Qiu, Y., Wang, C., Hu, Y., Kapoor, A., & Scherer, S. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341801 · Cited by 456 · 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

GMFlow: Learning Optical Flow via Global Matching

Xu, H., Zhang, J., Cai, J., Rezatofighi, H., & Tao, D. (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/cvpr52688.2022.00795 · Cited by 437 · see citing works

Playing for Benchmarks

Richter, S. R., Hayder, Z., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.243 · Cited by 434 · 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

VirtualHome: Simulating Household Activities Via Programs

Puig, X., Ra, K., Boben, M., Li, J., Wang, T., Fidler, S., & Torralba, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00886 · Cited by 378 · see citing works

Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness

Yu, J. J., Harley, A. W., & Derpanis, K. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_1 · Cited by 377 · see citing works

KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving

Choi, Y., Kim, N., Hwang, S., Park, K., Yoon, J. S., An, K., & Kweon, I. S. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2791533 · Cited by 370 · 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

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

Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing

Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1747642 · Cited by 312 · see citing works

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

Zhang, J., Zhao, X., Chen, Z., & Lu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2958671 · Cited by 304 · see citing works

ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape

Manhardt, F., Kehl, W., & Gaidon, A. (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.2019.00217 · Cited by 305 · see citing works

SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?

McCormac, J., Handa, A., Leutenegger, S., & Davison, A. J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.292 · Cited by 291 · see citing works

Learning Guided Convolutional Network for Depth Completion

Tang, J., Tian, F.-P., Feng, W., Li, J., & Tan, P. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3040528 · Cited by 294 · see citing works

Semantic segmentation using Vision Transformers: A survey

Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288 · see citing works

3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation

Ye, X., Li, J., Huang, H., Du, L., & Zhang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_25 · Cited by 287 · see citing works

Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

Prakash, A., Boochoon, S., Brophy, M., Acuna, D., Cameracci, E., State, G., Shapira, O., & Birchfield, S. T. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794443 · Cited by 287 · see citing works

Dissecting Person Re-Identification From the Viewpoint of Viewpoint

Sun, X., & Zheng, L. (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.2019.00070 · Cited by 284 · see citing works

SGM-Nets: Semi-Global Matching with Neural Networks

Seki, A., & Pollefeys, M. (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.2017.703 · Cited by 280 · see citing works

Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer

Atapour–Abarghouei, A., & Breckon, T. P. (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.2018.00296 · Cited by 278 · see citing works

Learning Motion Patterns in Videos

Tokmakov, P., Alahari, K., & Schmid, C. (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.2017.64 · Cited by 272 · see citing works

Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

Gustafsson, F., Danelljan, M., & Schön, T. B. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00167 · Cited by 272 · see citing works

UnrealCV: Connecting Computer Vision to Unreal Engine

Qiu, W., & Yuille, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_75 · Cited by 267 · see citing works

Fake it till you make it: face analysis in the wild using synthetic data alone

Wood, E., Baltrušaitis, T., Hewitt, C., Dziadzio, S., Cashman, T. J., & Shotton, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00366 · Cited by 257 · see citing works

Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · RWTH Publications (RWTH Aachen) · Cited by 254 · see citing works

Joint Monocular 3D Vehicle Detection and Tracking

Hu, H.-N., Cai, Q.-Z., Wang, D., Lin, J., Sun, M., Kraehenbuehl, P., Darrell, T., & Yu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00549 · Cited by 249 · see citing works

DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios

Yang, G., Song, X., Huang, C., Deng, Z., Shi, J., & Zhou, 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.2019.00099 · Cited by 253 · see citing works

Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset

Delmerico, J., Cieslewski, T., Rebecq, H., Faessler, M., & Scaramuzza, D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793887 · Cited by 238 · see citing works

Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

Chen, Y., Li, W., Chen, X., & 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.2019.00194 · Cited by 236 · see citing works

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

Manivasagam, S., Wang, S., Wong, K., Zeng, W., Sazanovich, M., Tan, S., Yang, B., Ma, W.-C., & Urtasun, R. (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/cvpr42600.2020.01118 · Cited by 241 · see citing works

Kubric: A scalable dataset generator

Greff, K., Belletti, F., Beyer, L., Doersch, C., Du, Y., Duckworth, D., Fleet, D. J., Gnanapragasam, D., Golemo, F., Herrmann, C., et al. (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/cvpr52688.2022.00373 · Cited by 235 · see citing works

Deep Learning on Point Clouds and Its Application: A Survey

Liu, W., Sun, J., Li, W., Hu, T., & Wang, P. (2019) · Sensors · doi:10.3390/s19194188 · Cited by 225 · see citing works

Co-fusion: Real-time segmentation, tracking and fusion of multiple objects

Rünz, M., & Agapito, L. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989518 · Cited by 230 · see citing works

Meta-Sim: Learning to Generate Synthetic DatasetsPreprint

(2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 228 · see citing works

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., & Markham, 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/cvpr46437.2021.00494 · Cited by 226 · see citing works

Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation

Kundu, A., Genova, K., Yin, X., Fathi, A., Pantofaru, C., Guibas, L. J., Tagliasacchi, A., Dellaert, F., & Funkhouser, T. A. (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/cvpr52688.2022.01253 · Cited by 223 · see citing works

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo, J., Kurup, U., & Shah, M. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2926042 · Cited by 233 · see citing works

Learning to Track with Object Permanence

Tokmakov, P., Li, J., Burgard, W., & Gaidon, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01068 · Cited by 226 · see citing works

Learning Monocular Depth by Distilling Cross-Domain Stereo Networks

Guo, X., Li, H., Yi, S., Ren, J., & Wang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_30 · Cited by 208 · see citing works

AADS: Augmented autonomous driving simulation using data-driven algorithms

Li, W., Pan, C., Zhang, R., Ren, J., Ma, Y., Fang, J., Yan, F., Geng, Q., Huang, X., Gong, H. J., et al. (2019) · Science Robotics · doi:10.1126/scirobotics.aaw0863 · Cited by 210 · see citing works

Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.90 · Cited by 280 · see citing works

Auxiliary Tasks in Multi-task LearningPreprint

Liebel, L., & Körner, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06334 · Cited by 269 · see citing works

Structure-Guided Ranking Loss for Single Image Depth Prediction

Xian, K., Zhang, J., Wang, O., Mai, L., Lin, Z., & Cao, Z. (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/cvpr42600.2020.00069 · Cited by 201 · see citing works

Cross-Domain Self-Supervised Multi-task Feature Learning Using Synthetic Imagery

Ren, Z., & Lee, Y. 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.2018.00086 · Cited by 192 · see citing works

Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation

Zhao, S., Fu, H., Gong, M., & Tao, 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.2019.01002 · Cited by 190 · see citing works

Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation

Lee, S., Kim, D.-W., Kim, N., & Jeong, S.-G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00018 · Cited by 188 · see citing works

A Survey on Deep Visual Place Recognition

Masone, C., & Barbara Caputo, B. (2021) · IEEE Access · doi:10.1109/access.2021.3054937 · Cited by 196 · see citing works

Multi-Modal 3D Object Detection in Autonomous Driving: A Survey

Wang, Y., Mao, Q., Zhu, H., Deng, J., Zhang, Y., Ji, J., Li, H., & Zhang, Y. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01784-z · Cited by 214 · see citing works

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., & Brox, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1082-6 · Cited by 191 · see citing works

AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation

Kundu, J. N., Uppala, P. K., Pahuja, A., & Babu, R. 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.2018.00281 · Cited by 184 · see citing works

Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation

Tremblay, J., To, T., & Birchfield, S. T. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00275 · Cited by 188 · see citing works

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

Liang, J., Jiang, L., Murphy, K. P., Yu, T., & Hauptmann, A. G. (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/cvpr42600.2020.01052 · Cited by 183 · see citing works

Learning Joint 2D-3D Representations for Depth Completion

Chen, Y., Yang, B., Liang, M., & Urtasun, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01012 · Cited by 179 · see citing works

Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications

Müller, M., Casser, V., Lahoud, J., Smith, N., & Ghanem, B. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1073-7 · Cited by 178 · see citing works

Modeling surface appearance from a single photograph using self-augmented convolutional neural networks

Li, X., Dong, Y., Peers, P., & Tong, X. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073641 · Cited by 174 · see citing works

Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

Dong, X., Garratt, M., Anavatti, S. G., & Abbass, H. A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3160741 · Cited by 173 · 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

Next-generation deep learning based on simulators and synthetic data

de Melo, C. M., Torralba, A. B., Guibas, L. J., DiCarlo, J. J., Chellappa, R., & Hodgins, J. K. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.11.008 · Cited by 173 · 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

Augmented LiDAR Simulator for Autonomous Driving

Fang, J., Zhou, D., Yan, F., Zhao, T., Zhang, F., Ma, Y., Wang, L., & Yang, R. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969927 · Cited by 163 · see citing works

Spatial Fusion GAN for Image Synthesis

Zhan, F., Zhu, H., & Lu, 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.2019.00377 · Cited by 160 · see citing works

CGIntrinsics: Better Intrinsic Image Decomposition Through Physically-Based Rendering

Li, Z., & Snavely, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_23 · Cited by 158 · see citing works

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Zhang, S., Li, J., Shi, L. X., Ding, M., Nguyen, D. C., Tan, W., Weng, J., & Han, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3324962 · Cited by 158 · see citing works

T$$^2$$Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks

Zheng, C., Cham, T., & Cai, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_47 · Cited by 154 · see citing works

Simulating Content Consistent Vehicle Datasets with Attribute Descent

Yao, Y., Zheng, L., Yang, X., Naphade, M., & Gedeon, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58539-6_46 · Cited by 154 · see citing works

RigNet: Repetitive Image Guided Network for Depth Completion

Yan, Z., Wang, K., Li, X., Zhang, Z., Li, J., & Yang, J. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_13 · Cited by 151 · see citing works

Supervising the New with the Old: Learning SFM from SFM

Klodt, M., & Vedaldi, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_43 · Cited by 147 · see citing works

Restricted Deformable Convolution-Based Road Scene Semantic Segmentation Using Surround View Cameras

Deng, L., Yang, M. Z., Li, H., Li, T., Hu, B. Q., & Wang, C. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2939832 · Cited by 143 · see citing works

Neural RGB®D Sensing: Depth and Uncertainty From a Video Camera

Liu, C., Gu, J., Kim, K., Narasimhan, S. G., & Kautz, 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.2019.01124 · Cited by 140 · see citing works

Photorealistic Image Synthesis for Object Instance Detection

Hodaň, T., Vineet, V., Gal, R., Shalev, E., Hanzelka, J., Connell, T., Urbina, P., Sinha, S. N., & Guenter, B. K. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803821 · Cited by 145 · see citing works

FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality

Guerra, W., Tal, E. A., Murali, V., Ryou, G., & Karaman, S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968116 · Cited by 139 · see citing works

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

Zhang, Y., David, P., Foroosh, H., & Gong, B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903401 · Cited by 142 · see citing works

RarePlanes: Synthetic Data Takes Flight

Shermeyer, J., Hossler, T., Etten, A. V., Hogan, D., Lewis, R., & Kim, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00025 · Cited by 142 · see citing works

Full Waveform LiDAR for Adverse Weather Conditions

Wallace, A. M., Halimi, A., & Buller, G. S. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2989148 · Cited by 143 · 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

DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

Shivakumar, S. S., Nguyen, T., Miller, I. D., Chen, S. W., Kumar, V., & Taylor, C. J. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917294 · Cited by 138 · 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

NOVA: Rendering Virtual Worlds with Humans for Computer Vision Tasks

Kerim, A., Aslan, C., Çelikcan, U., Erdem, E., & Erdem, A. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14271 · Cited by 12 · see citing works

A Generation Method and Verification of Virtual Dataset

Ding, P., Shen, Q., Huang, T., & Wang, M. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-6978-4_55 · Cited by 1 · see citing works

Using a virtual dataset for deep learning: improving real-world environment re-creation for human training

Thomas, T., Hurter, J., Winston, T., Reed, D., & Eifert, L. (2020) · The ...International conference on modeling and applied simulation · doi:10.46354/i3m.2020.mas.004

Training and testing object detectors with virtual images

Tian, Y., Li, X., Wang, K., & Wang, F. (2018) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510841 · Cited by 123 · see citing works