[1] CHEN C Y, SEFF A, KORNHAUSER A, et al. DeepDriving: Lear-ning Affordance for Direct Perception in Autonomous Driving // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 2722-2730.
[2] XU H Z, GAO Y, YU F, et al. End-to-End Learning of Driving Models from Large-Scale Video Datasets // Proc of the IEEE Confe-rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 3530-3538.
[3] BOJARSKI M, TESTA D D, DWORAKOWSKI D, et al. End to End Learning for Self-Driving Cars[C/OL]. [2023-09-16]. https://arxiv.org/pdf/1604.07316v1.pdf
[4] ONDRUŠKA P, POSNER I. Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks. Proceedings of the AAAI Confe-rence on Artificial Intelligence, 2016, 30(1): 3361-3367.
[5] CUI Y D, CHEN R, CHU W B, et al. Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(2): 722-739.
[6] LU N, CHENG N, ZHANG N, et al. Connected Vehicles: Solu-tions and Challenges. IEEE Internet of Things Journal, 2014, 1(4): 289-299.
[7] LUO R C, YIH C C, SU K L.Multisensor Fusion and Integration: Approaches, Applications, and Future Research Directions. IEEE Sensors journal, 2002, 2(2): 107-119.
[8] LOWE D G.Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60: 91-110.
[9] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An Efficient Alternative to SIFT or SURF // Proc of the International Conference on Computer Vision. Washington, USA: IEEE, 2011: 2564-2571.
[10] BAY H, TUYTELAARS T, VAN GOOL L.SURF: Speeded up Robust Features // Proc of the 9th European Conference on Computer Vision. Berlin, Germany: Springer, 2006: 404-417.
[11] FENG M D, HU S X, ANG M H, et al. 2D3D-Matchnet: Lear-ning to Match Keypoints Across 2D Image and 3D Point Cloud // Proc of the International Conference on Robotics and Automation. Washington, USA: IEEE, 2019: 4790-4796.
[12] LOWE D G.Object Recognition from Local Scale-Invariant Features // Proc of the 7th IEEE International Conference on Compu-ter Vision. Washington, USA: IEEE, 1999. DOI: 10.1109/ICCV.1999.790410.
[13] ZHONG Y.Intrinsic Shape Signatures: A Shape Descriptor for 3D Object Recognition // Proc of the IEEE 12th International Confe-rence on Computer Vision Workshops. Washington, USA: IEEE, 2009: 689-696.
[14] LI J X, LEE G H.DeepI2P: Image-to-Point Cloud Registration via Deep Classification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 15955-15964.
[15] BESL P J, MCKAY N D.A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
[16] BIBER P, STRAβER W. The Normal Distributions Transform: A New Approach to Laser Scan Matching // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Washing-ton, USA: IEEE, 2003: 2743-2748.
[17] YANG J L, LI H D, JIA Y D.Go-ICP: Solving 3D Registration Efficiently and Globally Optimally // Proc of the IEEE Internatio-nal Conference on Computer Vision. Washington, USA: IEEE, 2013: 1457-1464.
[18] LU W X, WAN G W, ZHOU Y, et al. DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration // Proc of the IEEE/CVF International Conference on Computer Vision. Wa-shington, USA: IEEE, 2019: 12-21.
[19] YEW Z J, LEE G H.RPM-Net: Robust Point Matching Using Learned Features // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 11821-11830.
[20] LI J X, LEE G H.USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 361-370.
[21] YEW Z J, LEE G H.3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 630-646.
[22] ZENG A, SONG S,NIEβNER M, et al. 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 199-208.
[23] GOJCIC Z, ZHOU C F, WEGNER J D, et al. The Perfect Match: 3D Point Cloud Matching with Smoothed Densities // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 5540-5549.
[24] TANG D Q, HU T J, SHEN L C, et al. AprilTag Array-Aided Extrinsic Calibration of Camera-Laser Multi-sensor System. Robotics and Biomimetics, 2016. DOI: 10.1186/s40638-016-0044-0.
[25] DHALL A, CHELANI K, RADHAKRISHNAN V, et al. LiDAR-Camera Calibration Using 3D-3D Point Correspondences[C/OL].[2023-09-16]. https://arxiv.org/abs/1705.09785.
[26] LI X C, XIAO Y X, WANG B B, et al. Automatic Targetless LiDAR-Camera Calibration: A Survey. Artificial Intelligence Review, 2023, 56(9): 9949-9987.
[27] ZHANG Z.A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
[28] RICHARDSON A, STROM J, OLSON E.AprilCal: Assisted and Repeatable Camera Calibration // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington, USA: IEEE, 2013: 1814-1821.
[29] YE X Q, SHU M, LI H Y, et al. Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 21309-21318.
[30] YU H B, LUO Y Z, SHU M, et al. DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2022: 21329-21338.
[31] YU H B, YANG W X, RUAN H Z, et al. V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 5486-5495.
[32] CREβ C, ZIMMER W, STRAND L, et al. A9-Dataset: Multisen-sor Infrastructure-Based Dataset for Mobility Research // Proc of the IEEE Intelligent Vehicles Symposium. Washington, USA: IEEE, 2022: 965-970.
[33] GEIGER A, LENZ P, URTASUN R.Are We Ready for Autonomous Driving? the KITTI Vision Benchmark Suite // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 3354-3361.
[34] CAESAR H, BANKITI V, LANG A H, et al. nuScenes: A Multimodal Dataset for Autonomous Driving // Proc of the IEEE/CVF conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 11618-11628.
[35] MEI J R, ZHU A Z, YAN X C, et al. Waymo Open Dataset: Pa-noramic Video Panoptic Segmentation // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 53-72.
[36] CHANG M F, LAMBERT J, SANGKLOY P, et al. Argoverse: 3D Tracking and Forecasting with Rich Maps // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 8740-8749.
[37] MAO J G, NIU M Z, JIANG C H, et al. One Million Scenes for Autonomous Driving: ONCE Dataset[C/OL].[2023-09-16]. https://arxiv.org/pdf/2106.11037.pdf.
[38] P?IBYL B, ZEM?ÍK P, ?ADÍK M. Absolute Pose Estimation from Line Correspondences Using Direct Linear Transformation. Computer Vision and Image Understanding, 2017, 161: 130-144.
[39] RANGANATHAN A. The Levenberg-Marquardt Algorithm[C/OL]. [2023-09-16]. http://www.ananth.in/docs/lmtut.pdf.
[40] GAO X S, HOU X R, TANG J L, et al. Complete Solution Classification for the Perspective-Three-Point Problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 930-943.
[41] LEPETIT V, MORENO-NOGUER F, FUA P.EPnP: An Accurate O(n) Solution to the PnP Problem. International Journal of Computer Vision, 2009, 81: 155-166.
[42] PENATE-SANCHEZ A, ANDRADE-CETTO J, MORENO-NOGUER F.Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2387-2400.
[43] FISCHLER M A, BOLLES R C.Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 1981, 24(6): 381-395.
[44] VISWANATHAN D G.Features from Accelerated Segment Test(FA-ST) [C/OL]. [2023-09-16].https://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV1011/AV1FeaturefromAcceleratedSegmentTest.pdf.
[45] CALONDER M, LEPETIT V, STRECHA C, et al. BRIEF: Binary Robust Independent Elementary Features // Proc of the 11th European Conference on Computer Vision. Berlin, Germany: Sprin-ger, 2010: 778-792.