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The 4D millimeter-wave (mmWave) radar, proficient in measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest within the autonomous driving community.
H. W. Kuhn, “The Hungarian method for the assignment problem,” Naval Research Logistics Quarterly , vol. 2, no. 1-2, pp. 83–97, Mar. 1955
1955
Earlier work this paper cites.
M. L. Puri and C. R. Rao, “Augmenting Shapiro-Wilk Test for Normality,” in Contribution to Applied Statistics , W. J. Ziegler, Ed. Basel: Birkhäuser Basel, 1976, vol. 22, pp. 129–139
1976
Earlier work this paper cites.
H. Rohling, “Radar CFAR Thresholding in Clutter and Multiple Target Situations,” IEEE Transactions on Aerospace and Electronic Systems , vol. AES-19, no. 4, pp. 608–621, Jul. 1983
1983
Earlier work this paper cites.
P. Biber and W. Strasser, “The normal distributions transform: A new approach to laser scan matching,” in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) , vol. 3. Las Vegas, Nevada, USA: IEEE, 2003, pp. 2743–2748
2003
Earlier work this paper cites.
A. Segal, D. Haehnel, and S. Thrun, “Generalized-ICP,” in Robotics: Science and Systems V , vol. 2. Seattle, WA, 2009, p. 435
2009
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Lecture Notes in Computer Science , ser. Lecture Notes in Computer Science, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 234–241
2015
Earlier work this paper cites.
P. Luc, C. Couprie, S. Chintala, and J. Verbeek, “Semantic Segmentation using Adversarial Networks,” in NIPS Workshop on Adversarial Training , 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Z. Pusztai and L. Hajder, “Accurate calibration of LiDAR-camera systems using ordinary boxes,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 394–402
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,” in Advances in Neural Information Processing Systems , vol. 30. Curran Associates, Inc., 2017
2017
Earlier work this paper cites.
S. Abdulatif, Q. Wei, F. Aziz, B. Kleiner, and U. Schneider, “Micro-doppler based human-robot classification using ensemble and deep learning approaches,” in 2018 IEEE Radar Conference (RadarConf18) , Apr. 2018, pp. 1043–1048
2018
Earlier work this paper cites.
A. Och, C. Pfeffer, J. Schrattenecker, S. Schuster, and R. Weigel, “A Scalable 77 GHz Massive MIMO FMCW Radar by Cascading Fully-Integrated Transceivers,” in 2018 Asia-Pacific Microwave Conference (APMC) , Nov. 2018, pp. 1235–1237
2018
Earlier work this paper cites.
W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert, “PCN: Point Completion Network,” in 2018 International Conference on 3D Vision (3DV) , Sep. 2018, pp. 728–737
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “SECOND: Sparsely Embedded Convolutional Detection,” Sensors , vol. 18, no. 10, p. 3337, Oct. 2018
2018
Earlier work this paper cites.
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3D Proposal Generation and Object Detection from View Aggregation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2018, pp. 1–8
2018
Earlier work this paper cites.
G. Kim and A. Kim, “Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Madrid: IEEE, Oct. 2018, pp. 4802–4809
2018
Earlier work this paper cites.
D. Brodeski, I. Bilik, and R. Giryes, “Deep Radar Detector,” in 2019 IEEE Radar Conference (RadarConf) , Apr. 2019, pp. 1–6
2019
Earlier work this paper cites.
I. Bilik, O. Longman, S. Villeval, and J. Tabrikian, “The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions,” IEEE Signal Processing Magazine , vol. 36, no. 5, pp. 20–31, Sep. 2019
2019
Earlier work this paper cites.
Z. Wu, L. Zhang, and H. Liu, “Generalized Three-Dimensional Imaging Algorithms for Synthetic Aperture Radar With Metamaterial Apertures-Based Antenna,” IEEE Access , vol. 7, pp. 59 716–59 727, 2019
2019
Earlier work this paper cites.
C. Schöller, M. Schnettler, A. Krämmer, G. Hinz, M. Bakovic, M. Güzet, and A. Knoll, “Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems,” Jul. 2019
2019
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “PointPillars: Fast Encoders for Object Detection From Point Clouds,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun. 2019, pp. 12 689–12 697
2019
Earlier work this paper cites.
M. Meyer and G. Kuschk, “Deep Learning Based 3D Object Detection for Automotive Radar and Camera,” in 2019 16th European Radar Conference (EuRAD) , Oct. 2019, pp. 133–136
2019
Earlier work this paper cites.
M. Meyer and G. Kuschk, “Automotive Radar Dataset for Deep Learning Based 3D Object Detection,” in 2019 16th European Radar Conference (EuRAD) , Oct. 2019, pp. 129–132
2019
Earlier work this paper cites.
K. Koide, J. Miura, E. Menegatti, H.-W. Cho, W. Kim, S. Choi, M. Eo, S. Khang, and J. Kim, “A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement,” International Journal of Advanced Robotic Systems , vol. 16, no. 2, p. 172988141984153, Mar. 2019
2019
Earlier work this paper cites.
T. Zhou, M. Yang, K. Jiang, H. Wong, and D. Yang, “MMW Radar-Based Technologies in Autonomous Driving: A Review,” Sensors , vol. 20, no. 24, p. 7283, Dec. 2020
2020
Earlier work this paper cites.
J. Jiang, Y. Li, L. Zhao, and X. Liu, “Wideband MIMO Directional Antenna Array with a Simple Meta-material Decoupling Structure for X-Band Applications,” The Applied Computational Electromagnetics Society Journal (ACES) , pp. 556–566, May 2020
2020
Earlier work this paper cites.
Y. Bao, T. Mahler, A. Pieper, A. Schreiber, and M. Schulze, “Motion Based Online Calibration for 4D Imaging Radar in Autonomous Driving Applications,” in 2020 German Microwave Conference (GeMiC) , Mar. 2020, pp. 108–111
2020
Earlier work this paper cites.
X. Weng, J. Wang, D. Held, and K. Kitani, “3D Multi-Object Tracking: A Baseline and New Evaluation Metrics,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2020, pp. 10 359–10 366
2020
Earlier work this paper cites.
F. E. Nowruzi, D. Kolhatkar, P. Kapoor, F. Al Hassanat, E. J. Heravi, R. Laganiere, J. Rebut, and W. Malik, “Deep Open Space Segmentation using Automotive Radar,” in 2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) , Nov. 2020, pp. 1–4
2020
Earlier work this paper cites.
A. Valada, R. Mohan, and W. Burgard, “Self-Supervised Model Adaptation for Multimodal Semantic Segmentation,” International Journal of Computer Vision , vol. 128, no. 5, pp. 1239–1285, May 2020
2020
Earlier work this paper cites.
C. X. Lu, M. R. U. Saputra, P. Zhao, Y. Almalioglu, P. P. B. de Gusmao, C. Chen, K. Sun, N. Trigoni, and A. Markham, “milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion,” Oct. 2020
2020
Earlier work this paper cites.
C. Doer and G. F. Trommer, “An EKF Based Approach to Radar Inertial Odometry,” in 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) , Sep. 2020, pp. 152–159
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuScenes: A Multimodal Dataset for Autonomous Driving,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Seattle, WA, USA: IEEE, Jun. 2020, pp. 11 618–11 628
2020
Earlier work this paper cites.
Y. Cheng, J. Su, H. Chen, and Y. Liu, “A New Automotive Radar 4D Point Clouds Detector by Using Deep Learning,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Jun. 2021, pp. 8398–8402
2021
Earlier work this paper cites.
R. Q. Charles, H. Su, M. Kaichun, L. J. Guibas, P. Ritter, M. Geyer, T. Gloekler, X. Gai, T. Schwarzenberger, G. Tretter, Y. Yu, and G. Vogel, “A Fully Integrated 78 GHz Automotive Radar System-an-Chip in 22nm FD-SOI CMOS,” in 2020 17th European Radar Conference (EuRAD) , Jan. 2021, pp. 57–60
2021
Earlier work this paper cites.
H.-W. Cho, W. Kim, S. Choi, M. Eo, S. Khang, and J. Kim, “Guided Generative Adversarial Network for Super Resolution of Imaging Radar,” in 2020 17th European Radar Conference (EuRAD) . New York: Ieee, Jan. 2021, pp. 144–147
2021
Earlier work this paper cites.
J. Domhof, J. F. P. Kooij, and D. M. Gavrila, “A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar,” IEEE Transactions on Intelligent Vehicles , vol. 6, no. 3, pp. 571–582, Sep. 2021
2021
Earlier work this paper cites.
E. Wise, J. Persic, C. Grebe, I. Petrovic, and J. Kelly, “A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , May 2021, pp. 13 164–13 170
2021
Earlier work this paper cites.
R. Q. Charles, H. Su, M. Kaichun, L. J. Guibas, P. Ritter, M. Geyer, T. Gloekler, X. Gai, T. Schwarzenberger, G. Tretter, Y. Yu, and G. Vogel, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” in 2020 17th European Radar Conference (EuRAD) . Honolulu, HI: IEEE, Jan. 2021, pp. 77–85
2021
Cited alongside, same era.
Y. Sun, Z. Huang, H. Zhang, Z. Cao, and D. Xu, “3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning,” in 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) , Oct. 2021, pp. 1–8
2021
Cited alongside, same era.
I. Orr, M. Cohen, and Z. Zalevsky, “High-resolution radar road segmentation using weakly supervised learning,” Nature Machine Intelligence , vol. 3, no. 3, pp. 239–246, Mar. 2021
2021
Cited alongside, same era.
B. Xu, X. Zhang, L. Wang, X. Hu, Z. Li, S. Pan, J. Li, and Y. Deng, “RPFA-Net: A 4D RaDAR Pillar Feature Attention Network for 3D Object Detection,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) , Sep. 2021, pp. 3061–3066
U. Chipengo, “High Fidelity Physics-Based Simulation of a 512-Channel 4D-Radar Sensor for Automotive Applications,” IEEE Access , vol. 11, pp. 15 242–15 251, 2023
2023
Closest in time.
B. Tan, L. Zheng, Z. Ma, J. Bai, X. Zhu, and L. Huang, “Learning-based 4D Millimeter Wave Automotive Radar Sensor Model Simulation for Autonomous Driving Scenarios,” in 2023 7th International Conference on Machine Vision and Information Technology (CMVIT) , Mar. 2023, pp. 123–128
2023
Closest in time.
L. Zheng, S. Li, B. Tan, L. Yang, S. Chen, L. Huang, J. Bai, X. Zhu, and Z. Ma, “RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection,” IEEE Transactions on Instrumentation and Measurement , vol. 72, pp. 1–14, 2023
2023
Closest in time.
B. Tan, Z. Ma, X. Zhu, S. Li, L. Zheng, S. Chen, L. Huang, and J. Bai, “3D Object Detection for Multiframe 4D Automotive Millimeter-Wave Radar Point Cloud,” IEEE Sensors Journal , vol. 23, no. 11, pp. 11 125–11 138, Jun. 2023
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2021
Cited alongside, same era.
H. Cui, J. Wu, J. Zhang, G. Chowdhary, and W. R. Norris, “3D Detection and Tracking for On-road Vehicles with a Monovision Camera and Dual Low-cost 4D mmWave Radars,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) , Sep. 2021, pp. 2931–2937
2021
Cited alongside, same era.
T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3D Object Detection and Tracking,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun. 2021, pp. 11 779–11 788
2021
Cited alongside, same era.
A. Zhang, F. E. Nowruzi, and R. Laganiere, “RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users,” in 2021 18th Conference on Robots and Vision (CRV) , vol. 19, May 2021, pp. 95–102
2021
Cited alongside, same era.
C. Doer and G. F. Trommer, “Radar Visual Inertial Odometry and Radar Thermal Inertial Odometry: Robust Navigation even in Challenging Visual Conditions,” in Gyroscopy and Navigation , Sep. 2021, pp. 331–338
2021
Cited alongside, same era.
C. Doer and G. F. Trommer, “Yaw aided Radar Inertial Odometry using Manhattan World Assumptions,” in 2021 28th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS) , May 2021, pp. 1–9
2021
Cited alongside, same era.
——, “X-RIO: Radar Inertial Odometry with Multiple Radar Sensors and Yaw Aiding,” Gyroscopy and Navigation , vol. 12, no. 4, pp. 329–339, Dec. 2021
2021
Cited alongside, same era.
J. Mao, M. Niu, C. Jiang, H. Liang, J. Chen, X. Liang, Y. Li, C. Ye, W. Zhang, Z. Li, J. Yu, H. Xu, and C. Xu, “One Million Scenes for Autonomous Driving: ONCE Dataset,” Oct. 2021
2021
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Cited alongside, same era.
2023
Closest in time.
L. Wang, X. Zhang, J. Li, B. Xv, R. Fu, H. Chen, L. Yang, D. Jin, and L. Zhao, “Multi-Modal and Multi-Scale Fusion 3D Object Detection of 4D Radar and LiDAR for Autonomous Driving,” IEEE Transactions on Vehicular Technology , vol. 72, no. 5, pp. 5628–5641, May 2023
2023
Closest in time.
Z. Pan, F. Ding, H. Zhong, and C. X. Lu, “Moving Object Detection and Tracking with 4D Radar Point Cloud,” Sep. 2023
2023
Closest in time.
B. Tan, Z. Ma, X. Zhu, S. Li, L. Zheng, L. Huang, and J. Bai, “Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments,” Remote Sensing , vol. 15, no. 11, p. 2923, Jan. 2023
2023
Closest in time.
F. Ding, A. Palffy, D. M. Gavrila, and C. X. Lu, “Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9340–9349
2023
Closest in time.
J. Li, C. Luo, and X. Yang, “PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun. 2023, pp. 17 567–17 576
2023
Closest in time.
W. Xiong, J. Liu, T. Huang, Q.-L. Han, Y. Xia, and B. Zhu, “LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion,” Aug. 2023
2023
Closest in time.
W. Shi, Z. Zhu, K. Zhang, H. Chen, Z. Yu, and Y. Zhu, “SMIFormer: Learning Spatial Feature Representation for 3D Object Detection from 4D Imaging Radar via Multi-View Interactive Transformers,” Sensors , vol. 23, no. 23, p. 9429, Jan. 2023
2023
Closest in time.
X. Chen, T. Zhang, Y. Wang, Y. Wang, and H. Zhao, “FUTR3D: A Unified Sensor Fusion Framework for 3D Detection,” Apr. 2023
2023
Closest in time.
Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , May 2023, pp. 2774–2781
2023
Closest in time.
J. Giroux, M. Bouchard, and R. Laganiere, “T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals,” Mar. 2023
2023
Closest in time.
B. Yang, I. Khatri, M. Happold, and C. Chen, “ADCNet: Learning from Raw Radar Data via Distillation,” Dec. 2023
2023
Closest in time.
T. Boot, N. Cazin, W. Sanberg, and J. Vanschoren, “Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets,” in 2023 IEEE Intelligent Vehicles Symposium (IV) , Jun. 2023, pp. 1–7
2023
Closest in time.
Y. Jin, A. Deligiannis, J.-C. Fuentes-Michel, and M. Vossiek, “Cross-Modal Supervision-Based Multitask Learning With Automotive Radar Raw Data,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 4, pp. 3012–3025, Apr. 2023
2023
Closest in time.
Y. Liu, F. Wang, N. Wang, and Z. Zhang, “Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion,” in Thirty-Seventh Conference on Neural Information Processing Systems , Nov. 2023
2023
Closest in time.
D.-H. Paek, S.-H. Kong, and K. T. Wijaya, “Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection,” in 2023 IEEE Intelligent Vehicles Symposium (IV) , Jun. 2023, pp. 1–6
2023
Closest in time.
S. Lu, G. Zhuo, L. Xiong, X. Zhu, L. Zheng, Z. He, M. Zhou, X. Lu, and J. Bai, “Efficient Deep-Learning 4D Automotive Radar Odometry Method,” IEEE Transactions on Intelligent Vehicles , pp. 1–15, 2023
2023
Closest in time.
J. Zhang, H. Zhuge, Z. Wu, G. Peng, M. Wen, Y. Liu, and D. Wang, “4DRadarSLAM: A 4D Imaging Radar SLAM System for Large-scale Environments based on Pose Graph Optimization,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , May 2023, pp. 8333–8340
2023
Closest in time.
X. Li, H. Zhang, and W. Chen, “4D Radar-Based Pose Graph SLAM With Ego-Velocity Pre-Integration Factor,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 5124–5131, Aug. 2023
2023
Closest in time.
A. Galeote-Luque, V. Kubelka, M. Magnusson, J.-R. Ruiz-Sarmiento, and J. Gonzalez-Jimenez, “Doppler-only Single-scan 3D Vehicle Odometry,” Oct. 2023
2023
Closest in time.
B. Wang, Y. Zhuang, and N. El-Bendary, “4D RADAR/IMU/GNSS INTEGRATED POSITIONING AND MAPPING FOR LARGE-SCALE ENVIRONMENTS,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. XLVIII-1/W2-2023, pp. 1223–1228, Dec. 2023
2023
Closest in time.
J. Zhang, R. Xiao, H. Li, Y. Liu, X. Suo, C. Hong, Z. Lin, and D. Wang, “4DRT-SLAM: Robust SLAM in Smoke Environments using 4D Radar and Thermal Camera based on Dense Deep Learnt Features,” in 10th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the 10th IEEE International Conference on Robotics, Automation and Mechatronics (RAM) , Jun. 2023
2023
Closest in time.
H. Chen, Y. Liu, and Y. Cheng, “DRIO: Robust Radar-Inertial Odometry in Dynamic Environments,” IEEE Robotics and Automation Letters , vol. 8, no. 9, pp. 5918–5925, Sep. 2023
2023
Closest in time.
X. Zhang, L. Wang, J. Chen, C. Fang, L. Yang, Z. Song, G. Yang, Y. Wang, X. Zhang, J. Li, Z. Li, Q. Yang, Z. Zhang, and S. S. Ge, “Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving,” Nov. 2023
2023
Closest in time.
J. Zhang, H. Zhuge, Y. Liu, G. Peng, Z. Wu, H. Zhang, Q. Lyu, H. Li, C. Zhao, D. Kircali, S. Mharolkar, X. Yang, S. Yi, Y. Wang, and D. Wang, “NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping,” Sep. 2023
2023
Closest in time.
A. Gu and T. Dao, “Mamba: Linear-Time Sequence Modeling with Selective State Spaces,” Dec. 2023
2023
Closest in time.
X. Lin, T. Lin, Z. Pei, L. Huang, and Z. Su, “Sparse4D v2: Recurrent Temporal Fusion with Sparse Model,” May 2023
2023
Closest in time.
X. Jiang, S. Li, Y. Liu, S. Wang, F. Jia, T. Wang, L. Han, and X. Zhang, “Far3D: Expanding the Horizon for Surround-view 3D Object Detection,” Aug. 2023
2023
Closest in time.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, pp. 1–14, 2023
2023
Closest in time.
X. Tian, T. Jiang, L. Yun, Y. Mao, H. Yang, Y. Wang, Y. Wang, and H. Zhao, “Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving,” Apr. 2023
2023
Closest in time.
X. Wang, Z. Zhu, W. Xu, Y. Zhang, Y. Wei, X. Chi, Y. Ye, D. Du, J. Lu, and X. Wang, “OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) , Oct. 2023, pp. 17 804–17 813
2023
Closest in time.
L. Fan, J. Wang, Y. Chang, Y. Li, Y. Wang, and D. Cao, “4D mmWave Radar for Autonomous Driving Perception: A Comprehensive Survey,” IEEE Transactions on Intelligent Vehicles , pp. 1–15, 2024
2024
Closest in time.
J. Liu, G. Ding, Y. Xia, J. Sun, T. Huang, L. Xie, and B. Zhu, “Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?” in IV 2024 , Apr. 2024
2024
Closest in time.
Y. Dalbah, J. Lahoud, and H. Cholakkal, “TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 353–362
2024
Closest in time.
Y. Liu, Y. Tian, Y. Zhao, H. Yu, L. Xie, Y. Wang, Q. Ye, and Y. Liu, “VMamba: Visual State Space Model,” Jan. 2024
2024
Closest in time.