Fetching the paper…
Reading the bibliography…
High-definition (HD) map provides abundant and precise static environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system.
Mallot, H.A., Bülthoff, H.H., Little, J., Bohrer, S.: Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biological cybernetics (1991)
1991
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009). Ieee
2009
Earlier work this paper cites.
Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems X, University of California (2014)
2014
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV (2017)
2017
Earlier work this paper cites.
Shan, T., Englot, B.: Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: IROS (2018)
2018
Earlier work this paper cites.
Acuna, D., Ling, H., Kar, A., Fidler, S.: Efficient interactive annotation of segmentation datasets with polygon-rnn++. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 859–868 (2018)
2018
Earlier work this paper cites.
Yan, Y., Mao, Y., Li, B.: Second: Sparsely embedded convolutional detection. Sensors 18
2018
Earlier work this paper cites.
Garnett, N., Cohen, R., Pe’er, T., Lahav, R., Levi, D.: 3d-lanenet: end-to-end 3d multiple lane detection. In: ICCV (2019)
2019
Earlier work this paper cites.
Xu, W., Wang, H., Qi, F., Lu, C.: Explicit shape encoding for real-time instance segmentation. In: ICCV (2019)
2019
Earlier work this paper cites.
Ling, H., Gao, J., Kar, A., Chen, W., Fidler, S.: Fast interactive object annotation with curve-gcn. In: CVPR (2019)
2019
Earlier work this paper cites.
Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: ICML (2019)
2019
Earlier work this paper cites.
Lee, Y., Hwang, J.-w., Lee, S., Bae, Y., Park, J.: 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, pp. 0–0 (2019)
2019
Earlier work this paper cites.
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: Fast encoders for object detection from point clouds. In: CVPR (2019)
2019
Earlier work this paper cites.
Shan, T., Englot, B.J., Meyers, D., Wang, W., Ratti, C., Rus, D.: LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping. In: IROS (2020)
2020
Earlier work this paper cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV (2020)
2020
Earlier work this paper cites.
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: CVPR (2020)
2020
Earlier work this paper cites.
Philion, J., Fidler, S.: Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In: ECCV (2020)
2020
Earlier work this paper cites.
Guo, Y., Chen, G., Zhao, P., Zhang, W., Miao, J., Wang, J., Choe, T.E.: Gen-lanenet: A generalized and scalable approach for 3d lane detection. In: European Conference on Computer Vision (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P.: Polarmask: Single shot instance segmentation with polar representation. In: CVPR (2020)
2020
Earlier work this paper cites.
Wei, F., Sun, X., Li, H., Wang, J., Lin, S.: Point-set anchors for object detection, instance segmentation and pose estimation. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16, pp. 527–544 (2020). Springer
2020
Earlier work this paper cites.
Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X.: Deep snake for real-time instance segmentation. In: CVPR (2020)
2020
Earlier work this paper cites.
Liu, R., Yuan, Z., Liu, T., Xiong, Z.: End-to-end lane shape prediction with transformers. In: WACV (2021)
2021
Earlier work this paper cites.
Can, Y.B., Liniger, A., Paudel, D.P., Van Gool, L.: Structured bird’s-eye-view traffic scene understanding from onboard images. In: ICCV (2021)
2021
Earlier work this paper cites.
Hu, A., Murez, Z., Mohan, N., Dudas, S., Hawke, J., Badrinarayanan, V., Cipolla, R., Kendall, A.: FIERY: Future instance segmentation in bird’s-eye view from surround monocular cameras. In: ICCV (2021)
2021
Earlier work this paper cites.
Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: Real-time attention-guided lane detection. In: CVPR (2021)
2021
Earlier work this paper cites.
Liu, L., Chen, X., Zhu, S., Tan, P.: Condlanenet: a top-to-down lane detection framework based on conditional convolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3773–3782 (2021)
2021
Earlier work this paper cites.
Liu, Z., Liew, J.H., Chen, X., Feng, J.: Dance: A deep attentive contour model for efficient instance segmentation. In: WACVW (2021)
2021
Earlier work this paper cites.
Xie, E., Wang, W., Ding, M., Zhang, R., Luo, P.: Polarmask++: Enhanced polar representation for single-shot instance segmentation and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2021
Earlier work this paper cites.
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: ICLR (2021)
2021
Earlier work this paper cites.
Fang, Y., Liao, B., Wang, X., Fang, J., Qi, J., Wu, R., Niu, J., Liu, W.: You only look at one sequence: Rethinking transformer in vision through object detection. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., Sun, L., Wang, J.: Conditional detr for fast training convergence. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 3631–3640 (2021)
2021
Earlier work this paper cites.
Fang, Y., Yang, S., Wang, X., Li, Y., Fang, C., Shan, Y., Feng, B., Liu, W.: Instances as queries. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6910–6919 (2021)
2021
Earlier work this paper cites.
Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., Tu, Z.: Pose recognition with cascade transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1944–1953 (2021)
2021
Earlier work this paper cites.
Li, Y., Zhang, S., Wang, Z., Yang, S., Yang, W., Xia, S., Zhou, E.: Tokenpose: Learning keypoint tokens for human pose estimation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 11293–11302 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
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)
2021
Cited alongside, same era.
Park, D., Ambrus, R., Guizilini, V., Li, J., Gaidon, A.: Is pseudo-lidar needed for monocular 3d object detection? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3142–3152 (2021)
2021
Cited alongside, same era.
2023
Closest in time.
Dauner, D., Hallgarten, M., Geiger, A., Chitta, K.: Parting with misconceptions about learning-based vehicle motion planning. arXiv 2306.07962
2023
Closest in time.
Pan, C., He, Y., Peng, J., Zhang, Q., Sui, W., Zhang, Z.: Baeformer: Bi-directional and early interaction transformers for bird’s eye view semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9590–9599 (2023)
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chen, L., Sima, C., Li, Y., Zheng, Z., Xu, J., Geng, X., Li, H., He, C., Shi, J., Qiao, Y., Yan, J.: Persformer: 3d lane detection via perspective transformer and the openlane benchmark. In: ECCV (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Zhou, B., Krähenbühl, P.: Cross-view transformers for real-time map-view semantic segmentation. In: CVPR (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: ECCV (2022)
2022
Cited alongside, same era.
Li, Q., Wang, Y., Wang, Y., Zhao, H.: Hdmapnet: An online hd map construction and evaluation framework. In: ICRA (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Qiao, L., Ding, W., Qiu, X., Zhang, C.: End-to-end vectorized hd-map construction with piecewise bezier curve. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13218–13228 (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Xiong, X., Liu, Y., Yuan, T., Wang, Y., Wang, Y., Zhao, H.: Neural map prior for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17535–17544 (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Jiang, B., Chen, S., Xu, Q., Liao, B., Chen, J., Zhou, H., Zhang, Q., Liu, W., Huang, C., Wang, X.: Vad: Vectorized scene representation for efficient autonomous driving. ICCV (2023)
2023
Closest in time.
Ding, W., Qiao, L., Qiu, X., Zhang, C.: Pivotnet: Vectorized pivot learning for end-to-end hd map construction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3672–3682 (2023)
2023
Closest in time.
Wang, R., Qin, J., Li, K., Li, Y., Cao, D., Xu, J.: Bev-lanedet: An efficient 3d lane detection based on virtual camera via key-points. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1002–1011 (2023)
2023
Closest in time.
2023
Closest in time.
Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L., Shum, H.-Y.: DINO: DETR with improved denoising anchor boxes for end-to-end object detection. In: The Eleventh International Conference on Learning Representations (2023). https://openreview.net/forum?id=3mRwyG5one
2023
Closest in time.
Jia, D., Yuan, Y., He, H., Wu, X., Yu, H., Lin, W., Sun, L., Zhang, C., Hu, H.: Detrs with hybrid matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19702–19712 (2023)
2023
Closest in time.
Li, F., Zhang, H., Xu, H., Liu, S., Zhang, L., Ni, L.M., Shum, H.-Y.: Mask dino: Towards a unified transformer-based framework for object detection and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3041–3050 (2023)
2023
Closest in time.
Mao, J., Shi, S., Wang, X., Li, H.: 3d object detection for autonomous driving: A comprehensive survey. International Journal of Computer Vision 131
2023
Closest in time.
Wang, Y., Mao, Q., Zhu, H., Deng, J., Zhang, Y., Ji, J., Li, H., Zhang, Y.: Multi-modal 3d object detection in autonomous driving: a survey. International Journal of Computer Vision 131
2023
Closest in time.
Shi, S., Jiang, L., Deng, J., Wang, Z., Guo, C., Shi, J., Wang, X., Li, H.: Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection. International Journal of Computer Vision 131
2023
Closest in time.
Jia, D., Yuan, Y., He, H., Wu, X., Yu, H., Lin, W., Sun, L., Zhang, C., Hu, H.: Detrs with hybrid matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19702–19712 (2023)
2023
Closest in time.
Gu, X., Song, G., Gilitschenski, I., Pavone, M., Ivanovic, B.: Producing and leveraging online map uncertainty in trajectory prediction. In: CVPR (2024)
2024
Closest in time.
Yuan, T., Liu, Y., Wang, Y., Wang, Y., Zhao, H.: Streammapnet: Streaming mapping network for vectorized online hd map construction. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 7356–7365 (2024)
2024
Closest in time.
Li, T., Jia, P., Wang, B., Chen, L., JIANG, K., Yan, J., Li, H.: Lanesegnet: Map learning with lane segment perception for autonomous driving. In: The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=LsURkIPYR5
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.