Fetching the paper…
Reading the bibliography…
Multi-camera 3D object detection for autonomous driving is a challenging problem that has garnered notable attention from both academia and industry.
Deng J, Dong W, Socher R, et al (2009) ImageNet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, Ieee
2009
Earlier work this paper cites.
Tellex S, Kollar T, Dickerson S, et al (2011) Understanding natural language commands for robotic navigation and mobile manipulation. In: AAAI Conference on Artificial Intelligence, pp 1507–1514
2011
Earlier work this paper cites.
Romero A, Ballas N, Kahou SE, et al (2014) Fitnets: Hints for thin deep nets. arXiv preprint arXiv:14126550
2014
Earlier work this paper cites.
Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network (2015). arXiv preprint arXiv:150302531 2
2015
Earlier work this paper cites.
Gupta S, Hoffman J, Malik J (2016) Cross modal distillation for supervision transfer. In: IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
Chen G, Choi W, Yu X, et al (2017) Learning efficient object detection models with knowledge distillation. Advances in Neural Information Processing Systems 30
2017
Earlier work this paper cites.
He K, Gkioxari G, Dollár P, et al (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
2017
Earlier work this paper cites.
Yan Y, Mao Y, Li B (2018) SECOND: Sparsely embedded convolutional detection. Sensors 18(10)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Lang AH, Vora S, Caesar H, et al (2019) PointPillars: Fast encoders for object detection from point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition
2019
Earlier work this paper cites.
Lee Y, Hwang Jw, Lee S, et al (2019) An energy and gpu-computation efficient backbone network for real-time object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
2019
Earlier work this paper cites.
Wang T, Yuan L, Zhang X, et al (2019) Distilling object detectors with fine-grained feature imitation. In: IEEE Conference on Computer Vision and Pattern Recognition
2019
Earlier work this paper cites.
Caesar H, Bankiti V, Lang AH, et al (2020) nuScenes: A multimodal dataset for autonomous driving. In: IEEE Conference on Computer Vision and Pattern Recognition
2020
Earlier work this paper cites.
Liu Y, Wang Y, Wang S, et al (2020) CBNet: A novel composite backbone network architecture for object detection. In: AAAI Conference on Artificial Intelligence
2020
Cited alongside, same era.
Philion J, Fidler S (2020) Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In: European Conference on Computer Vision, Springer
2020
Cited alongside, same era.
Sun P, Kretzschmar H, Dotiwalla X, et al (2020) Scalability in perception for autonomous driving: Waymo open dataset. In: IEEE Conference on Computer Vision and Pattern Recognition
2020
Cited alongside, same era.
Xie S, Gu J, Guo D, et al (2020) Pointcontrast: Unsupervised pre-training for 3d point cloud understanding. In: European Conference on Computer Vision, Springer
2020
Cited alongside, same era.
Dai X, Jiang Z, Wu Z, et al (2021) General instance distillation for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition
Yin T, Zhou X, Krahenbuhl P (2021) Center-based 3d object detection and tracking. In: IEEE Conference on Computer Vision and Pattern Recognition
2021
Later among the works it cites.
Zhang Z, Girdhar R, Joulin A, et al (2021) Self-supervised pretraining of 3d features on any point-cloud. In: IEEE International Conference on Computer Vision
2021
Later among the works it cites.
Bai X, Hu Z, Zhu X, et al (2022) TransFusion: Robust lidar-camera fusion for 3d object detection with transformers. In: IEEE Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
Cho H, Choi J, Baek G, et al (2022) itKD: Interchange transfer-based knowledge distillation for 3d object detection. arXiv preprint arXiv:220515531
2022
Later among the works it cites.
Chong Z, Ma X, Zhang H, et al (2022) MonoDistill: Learning spatial features for monocular 3d object detection. arXiv preprint arXiv:220110830
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Guo X, Shi S, Wang X, et al (2021) LIGA-Stereo: Learning lidar geometry aware representations for stereo-based 3d detector. In: IEEE International Conference on Computer Vision
2021
Cited alongside, same era.
Hou J, Xie S, Graham B, et al (2021) Pri3d: Can 3d priors help 2d representation learning? In: IEEE International Conference on Computer Vision
2021
Cited alongside, same era.
Huang J, Huang G, Zhu Z, et al (2021) BEVDet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv preprint arXiv:211211790
2021
Cited alongside, same era.
Kang Z, Zhang P, Zhang X, et al (2021) Instance-conditional knowledge distillation for object detection. Advances in Neural Information Processing Systems 34:16468–16480
2021
Cited alongside, same era.
Liu YC, Huang YK, Chiang HY, et al (2021) Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining. arXiv preprint arXiv:210404687
2021
Cited alongside, same era.
Park D, Ambrus R, Guizilini V, et al (2021) Is pseudo-lidar needed for monocular 3d object detection? In: IEEE International Conference on Computer Vision
2021
Cited alongside, same era.
Shu C, Liu Y, Gao J, et al (2021) Channel-wise knowledge distillation for dense prediction. In: Proceedings of the IEEE international conference on computer vision, pp 5311–5320
2021
Cited alongside, same era.
2022
Later among the works it cites.
Huang J, Huang G (2022) BEVDet4D: Exploit temporal cues in multi-camera 3d object detection. arXiv preprint arXiv:220317054
2022
Later among the works it cites.
Jiang Y, Zhang L, Miao Z, et al (2022) Polarformer: Multi-camera 3d object detection with polar transformers. arXiv preprint arXiv:220615398
2022
Later among the works it cites.
Liang T, Xie H, Yu K, et al (2022) BEVFusion: A simple and robust lidar-camera fusion framework. arXiv preprint arXiv:220513790
2022
Later among the works it cites.
Park J, Xu C, Yang S, et al (2022) Time Will Tell: New outlooks and a baseline for temporal multi-view 3d object detection. arXiv preprint arXiv:221002443
2022
Later among the works it cites.
Peng L, Liu F, Yu Z, et al (2022) Lidar point cloud guided monocular 3d object detection. In: European Conference on Computer Vision, Springer, pp 123–139
2022
Later among the works it cites.
Sautier C, Puy G, Gidaris S, et al (2022) Image-to-lidar self-supervised distillation for autonomous driving data. In: IEEE Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
Wu P, Jia X, Chen L, et al (2022) Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline. In: Advances in Neural Information Processing Systems
2022
Later among the works it cites.
Wu P, Chen L, Li H, et al (2023) Policy pre-training for autonomous driving via self-supervised geometric modeling. In: The Eleventh International Conference on Learning Representations
2023
Closest in time.