Fetching the paper…
Reading the bibliography…
Recently, self-supervised representation learning relying on vast amounts of unlabeled data has been explored as a pre-training method for autonomous driving.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
Earlier work this paper cites.
Submanifold sparse convolutional networks
Graham, B.; and Van der Maaten, L. 2017 · 2017
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features
Caron, M.; Bojanowski, P.; Joulin, A.; and Douze, M. 2018 · 2018
Earlier work this paper cites.
Second: Sparsely embedded convolutional detection
Yan, Y.; Mao, Y.; and Li, B. 2018 · 2018
Earlier work this paper cites.
Scaling and benchmarking self-supervised visual representation learning
Goyal, P.; Mahajan, D.; Gupta, A.; and Misra, I. 2019 · 2019
Earlier work this paper cites.
Pointrcnn: 3d object proposal generation and detection from point cloud
Shi, S.; Wang, X.; and Li, H. 2019 · 2019
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Caron, M.; Misra, I.; Mairal, J.; Goyal, P.; Bojanowski, P.; and Joulin, A. 2020 · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P.; Buchatskaya, E.; Doersch, C.; Avila Pires, B.; Guo, Z.; Gheshlaghi Azar, M.; et al. 2020 · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
Earlier work this paper cites.
Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
Shi, S.; Guo, C.; Jiang, L.; Wang, Z.; Shi, J.; Wang, X.; and Li, H. 2020 · 2020
Earlier work this paper cites.
Scalability in perception for autonomous driving: Waymo open dataset
Sun, P.; Kretzschmar, H.; Dotiwalla, X.; Chouard, A.; Patnaik, V.; Tsui, P.; Guo, J.; Zhou, Y.; Chai, Y.; Caine, B.; et al. 2020 · 2020
Earlier work this paper cites.
OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds
Team, O. D. 2020 · 2020
Earlier work this paper cites.
Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Xie, S.; Gu, J.; Guo, D.; Qi, C. R.; Guibas, L.; and Litany, O. 2020 · 2020
Earlier work this paper cites.
Emerging properties in self-supervised vision transformers
Caron, M.; Touvron, H.; Misra, I.; Jégou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
Earlier work this paper cites.
Exploring simple siamese representation learning
Chen, X.; and He, K. 2021 · 2021
Earlier work this paper cites.
Voxel r-cnn: Towards high performance voxel-based 3d object detection
Deng, J.; Shi, S.; Li, P.; Zhou, W.; Zhang, Y.; and Li, H. 2021 · 2021
Cited alongside, same era.
Spatio-temporal self-supervised representation learning for 3d point clouds
Huang, S.; Xie, Y.; Zhu, S.-C.; and Zhu, Y. 2021 · 2021
Cited alongside, same era.
Exploring geometry-aware contrast and clustering harmonization for self-supervised 3d object detection
Liang, H.; Jiang, C.; Feng, D.; Chen, X.; Xu, H.; Liang, X.; Zhang, W.; Li, Z.; and Van Gool, L. 2021 · 2021
Cited alongside, same era.
One million scenes for autonomous driving: Once dataset
Mao, J.; Niu, M.; Jiang, C.; Liang, H.; Chen, J.; Liang, X.; Li, Y.; Ye, C.; Zhang, W.; Li, Z.; et al. 2021 · 2021
Cited alongside, same era.
Center-based 3d object detection and tracking
Yin, T.; Zhou, X.; and Krahenbuhl, P. 2021 · 2021
Cited alongside, same era.
Occupancy-mae: Self-supervised pre-training large-scale lidar point clouds with masked occupancy autoencoders
Min, C.; Xiao, L.; Zhao, D.; Nie, Y.; and Dai, B. 2023 · 2023
Later among the works it cites.
Temporal consistent 3D lidar representation learning for semantic perception in autonomous driving
Nunes, L.; Wiesmann, L.; Marcuzzi, R.; Chen, X.; Behley, J.; and Stachniss, C. 2023 · 2023
Later among the works it cites.
Spatiotemporal self-supervised learning for point clouds in the wild
Wu, Y.; Zhang, T.; Ke, W.; Süsstrunk, S.; and Salzmann, M. 2023 · 2023
Later among the works it cites.
Mv-jar: Masked voxel jigsaw and reconstruction for lidar-based self-supervised pre-training
Xu, R.; Wang, T.; Zhang, W.; Chen, R.; Cao, J.; Pang, J.; and Lin, D. 2023 · 2023
Later among the works it cites.
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Agro, B.; Sykora, Q.; Casas, S.; Gilles, T.; and Urtasun, R. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J.; Jing, L.; Misra, I.; LeCun, Y.; and Deny, S. 2021 · 2021
Cited alongside, same era.
Self-supervised pretraining of 3d features on any point-cloud
Zhang, Z.; Girdhar, R.; Joulin, A.; and Misra, I. 2021 · 2021
Cited alongside, same era.
VICReg: Variance-Invariance-Covariance Regularization For Self-Supervised Learning
Bardes, A.; Ponce, J.; and LeCun, Y. 2022 · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
Cited alongside, same era.
A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27
LeCun, Y. 2022 · 2022
Cited alongside, same era.
Understanding collapse in non-contrastive siamese representation learning
Li, A. C.; Efros, A. A.; and Pathak, D. 2022 · 2022
Cited alongside, same era.
SegContrast: 3D point cloud feature representation learning through self-supervised segment discrimination
Nunes, L.; Marcuzzi, R.; Chen, X.; Behley, J.; and Stachniss, C. 2022 · 2022
Cited alongside, same era.
On pretraining data diversity for self-supervised learning
Al Kader Hammoud, H. A.; Das, T.; Pizzati, F.; Torr, P. H.; Bibi, A.; and Ghanem, B. 2024 · 2024
Later among the works it cites.
Revisiting feature prediction for learning visual representations from video
Bardes, A.; Garrido, Q.; Ponce, J.; Chen, X.; Rabbat, M.; LeCun, Y.; Assran, M.; and Ballas, N. 2024 · 2024
Later among the works it cites.
BEV-MAE: Bird’s Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Lin, Z.; Wang, Y.; Qi, S.; Dong, N.; and Yang, M.-H. 2024 · 2024
Later among the works it cites.
Bevcontrast: Self-supervision in bev space for automotive lidar point clouds
Sautier, C.; Puy, G.; Boulch, A.; Marlet, R.; and Lepetit, V. 2024 · 2024
Later among the works it cites.
Visual point cloud forecasting enables scalable autonomous driving
Yang, Z.; Chen, L.; Sun, Y.; and Li, H. 2024 · 2024
Later among the works it cites.
Ad-pt: Autonomous driving pre-training with large-scale point cloud dataset
Yuan, J.; Zhang, B.; Yan, X.; Shi, B.; Chen, T.; Li, Y.; and Qiao, Y. 2024 · 2024
Later among the works it cites.
Multi-View Radar Autoencoder for Self-Supervised Automotive Radar Representation Learning
Zhu, H.; He, H.; Choromanska, A.; Ravindran, S.; Shi, B.; and Chen, L. 2024 · 2024
Later among the works it cites.
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds
Abdelsamad, M.; Ulrich, M.; Gläser, C.; and Valada, A. 2025 · 2025
Closest in time.
V-jepa 2: Self-supervised video models enable understanding, prediction and planning
Assran, M.; Bardes, A.; Fan, D.; Garrido, Q.; Howes, R.; Muckley, M.; Rizvi, A.; Roberts, C.; Sinha, K.; Zholus, A.; et al. 2025 · 2025
Closest in time.
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection
Hegde, D.; Lohit, S.; Peng, K.-C.; Jones, M. J.; and Patel, V. M. 2025 · 2025
Closest in time.
T-MAE: temporal masked autoencoders for point cloud representation learning
Wei, W.; Nejadasl, F. K.; Gevers, T.; and Oswald, M. R. 2025 · 2025
Closest in time.