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Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks essential for autonomous driving.
Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 1907–1915 (2017)
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Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 605–613 (2017)
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Liang, M., Yang, B., Wang, S., Urtasun, R.: Deep continuous fusion for multi-sensor 3d object detection. In: Proceedings of the European conference on computer vision (ECCV). pp. 641–656 (2018)
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Yan, Y., Mao, Y., Li, B.: Second: Sparsely embedded convolutional detection. Sensors 18
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Sindagi, V.A., Zhou, Y., Tuzel, O.: Mvx-net: Multimodal voxelnet for 3d object detection. In: 2019 International Conference on Robotics and Automation (ICRA). pp. 7276–7282. IEEE (2019)
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Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11621–11631 (2020)
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Philion, J., Fidler, S.: Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16. pp. 194–210. Springer (2020)
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Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2446–2454 (2020)
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Vora, S., Lang, A.H., Helou, B., Beijbom, O.: Pointpainting: Sequential fusion for 3d object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4604–4612 (2020)
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 10012–10022 (2021)
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Nabati, R., Qi, H.: Centerfusion: Center-based radar and camera fusion for 3d object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1527–1536 (2021)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
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Wang, C., Ma, C., Zhu, M., Yang, X.: Pointaugmenting: Cross-modal augmentation for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11794–11803 (2021)
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Yin, T., Zhou, X., Krahenbuhl, P.: Center-based 3d object detection and tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11784–11793 (2021)
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Yin, T., Zhou, X., Krähenbühl, P.: Multimodal virtual point 3d detection. Advances in Neural Information Processing Systems 34
2021
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Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., Tai, C.L.: Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1090–1099 (2022)
2022
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Chen, Y., Li, Y., Zhang, X., Sun, J., Jia, J.: Focal sparse convolutional networks for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5428–5437 (2022)
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Fan, L., Pang, Z., Zhang, T., Wang, Y.X., Zhao, H., Wang, F., Wang, N., Zhang, Z.: Embracing single stride 3d object detector with sparse transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8458–8468 (2022)
2022
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He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 16000–16009 (2022)
2022
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Huang, L., You, S., Zheng, M., Wang, F., Qian, C., Yamasaki, T.: Green hierarchical vision transformer for masked image modeling. Advances in Neural Information Processing Systems 35
2022
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2022
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Chen, X., Zhang, T., Wang, Y., Wang, Y., Zhao, H.: Futr3d: A unified sensor fusion framework for 3d detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 172–181 (2023)
2023
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Chen, Y., Yu, Z., Chen, Y., Lan, S., Anandkumar, A., Jia, J., Alvarez, J.M.: Focalformer3d: focusing on hard instance for 3d object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8394–8405 (2023)
2023
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Chen, Y., Liu, J., Zhang, X., Qi, X., Jia, J.: Largekernel3d: Scaling up kernels in 3d sparse cnns. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13488–13498 (2023)
2023
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Chi, X., Liu, J., Lu, M., Zhang, R., Wang, Z., Guo, Y., Zhang, S.: Bev-san: Accurate bev 3d object detection via slice attention networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17461–17470 (2023)
2023
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Li, Y., Yu, A.W., Meng, T., Caine, B., Ngiam, J., Peng, D., Shen, J., Lu, Y., Zhou, D., Le, Q.V., et al.: Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17182–17191 (2022)
2022
Cited alongside, same era.
Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Qiao, Y., Dai, J.: Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In: European conference on computer vision. pp. 1–18. Springer (2022)
2022
Cited alongside, same era.
Liang, T., Xie, H., Yu, K., Xia, Z., Lin, Z., Wang, Y., Tang, T., Wang, B., Tang, Z.: Bevfusion: A simple and robust lidar-camera fusion framework. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Liu, H., Cai, M., Lee, Y.J.: Masked discrimination for self-supervised learning on point clouds. In: European Conference on Computer Vision. pp. 657–675. Springer (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Pang, Y., Wang, W., Tay, F.E., Liu, W., Tian, Y., Yuan, L.: Masked autoencoders for point cloud self-supervised learning. In: European conference on computer vision. pp. 604–621. Springer (2022)
2022
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Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G.: Vision transformer with deformable attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4794–4803 (2022)
2022
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2022
Cited alongside, same era.
Closest in time.
Hess, G., Jaxing, J., Svensson, E., Hagerman, D., Petersson, C., Svensson, L.: Masked autoencoder for self-supervised pre-training on lidar point clouds. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 350–359 (2023)
2023
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2023
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Jiao, Y., Jie, Z., Chen, S., Chen, J., Ma, L., Jiang, Y.G.: Msmdfusion: Fusing lidar and camera at multiple scales with multi-depth seeds for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21643–21652 (2023)
2023
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Li, H., Sima, C., Dai, J., Wang, W., Lu, L., Wang, H., Zeng, J., Li, Z., Yang, J., Deng, H., et al.: Delving into the devils of bird’s-eye-view perception: A review, evaluation and recipe. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
Closest in time.
Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D.L., Han, S.: Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 2774–2781. IEEE (2023)
2023
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2023
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Tian, X., Ran, H., Wang, Y., Zhao, H.: Geomae: Masked geometric target prediction for self-supervised point cloud pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13570–13580 (2023)
2023
Closest in time.
Wang, H., Tang, H., Shi, S., Li, A., Li, Z., Schiele, B., Wang, L.: Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view representation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6792–6802 (2023)
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2023
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2023
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Xu, R., Wang, T., Zhang, W., Chen, R., Cao, J., Pang, J., Lin, D.: Mv-jar: Masked voxel jigsaw and reconstruction for lidar-based self-supervised pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13445–13454 (2023)
2023
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Yang, C., Chen, Y., Tian, H., Tao, C., Zhu, X., Zhang, Z., Huang, G., Li, H., Qiao, Y., Lu, L., et al.: Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17830–17839 (2023)
2023
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Yang, H., He, T., Liu, J., Chen, H., Wu, B., Lin, B., He, X., Ouyang, W.: Gd-mae: generative decoder for mae pre-training on lidar point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9403–9414 (2023)
2023
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Zhang, R., Wang, L., Qiao, Y., Gao, P., Li, H.: Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21769–21780 (2023)
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