ICCK Transactions on Sensing, Communication, and Control Home About Editors Current Issue
-
CiteScore
-
Impact Factor
  1. Home
  2. Journals
  3. ICCK Transactions on Sensing, Communication, and Control
  4. Volume 1, Issue 1
Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
Review Article | Published: 12 October 2024
ICCK Transactions on Sensing, Communication, and Control Volume 1, Issue 1, 2024: 3-29
Volume 1, Issue 1, ICCK Transactions on Sensing, Communication, and Control
Volume 1, Issue 1, 2024
Submit Manuscript Edit a Special Issue
Academic Editor
Quanmin Zhu
Quanmin Zhu
University of the West of England, United Kingdom
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes Reservoir Science: A Multi-Coupling Communication Platform to Promote Energy Transformation, Climate Change and Environmental Protection From CO$_2$ Sequestration to Hydrogen Storage: Further Utilization of Depleted Gas Reservoirs Reinforcement Learning for Prompt Optimization in Language Models: A Comprehensive Survey of Methods, Representations, and Evaluation Challenges Effects of Crosslinking Agents and Reservoir Conditions on the Propagation of Fractures in Coal Reservoirs During Hydraulic Fracturing AI and the Future of Education: Advancing Personalized Learning and Intelligent Tutoring Systems The Influence of Geological Factors and Transmission Fluids on the Exploitation of Reservoir Geothermal Resources: Factor Discussion and Mechanism Analysis YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Plant Disease Detection Using Deep Learning Techniques Current Status and Development Prospects of Carbon Capture, Utilization, and Storage (CCUS) in China: Technical, Policy, and Market Perspectives
ICCK Transactions on Sensing, Communication, and Control, Volume 1, Issue 1, 2024: 3-29

Free to Read | Review Article | 12 October 2024
Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
by
Ghulam E Mustafa Abro Ghulam E Mustafa Abro 1 *
Zain Anwar Ali Zain Anwar Ali 2
Summaiya Rajput Summaiya Rajput 3
1 Interdisciplinary Research Centre for Aviation and Space Exploration (IRCASE), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Kingdom of Saudi Arabia
2 Electronic Engineering Department, Maynooth International Engineering College (MIEC), Maynooth University, Maynooth, Co. Kildare, Ireland
3 Department of Telecommunication Engineering, Mehran University of Engineering and Technology (MUET), Pakistan
* Corresponding Author: Ghulam E Mustafa Abro, [email protected]
DOI: 10.62762/TSCC.2024.989358
ARK: ark:/57805/tscc.2024.989358
Received: 13 September 2024, Accepted: 22 September 2024, Published: 12 October 2024  
Cited by: 12  (Source: Web of Science), 13  (Source: Scopus ), 14  (Source: Google Scholar)
   PDF  (2.08 MB)
Article Metrics Cite This Article
Abstract
This review paper offers a thorough assessment of three-dimensional object recognition methods, an essential element in the perception frameworks of autonomous systems. This analysis emphasises the integration of LiDAR and camera sensors, providing a distinctive contrast with more economical alternatives like camera-only or camera-Radar combinations. This study objectively evaluates performance and practical implementation issues, such as cost and operational efficiency, thereby elucidating the limitations of existing systems and proposing avenues for further research. The insights provided render it a significant asset for enhancing 3D object recognition and autonomy in intelligent systems.
Graphical Abstract
Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
Keywords
autonomous systems
camera
fusion methods
LiDAR
object detection
radar and three-dimensional
Funding
This work was supported by Interdisciplinary Research Centre for Aviation and Space Exploration (IRCASE), King Fahd University of Petroleum and Minerals (KFUPM, Kingdom of Saudi Arabia).
References
  1. Banham, M. R., & Katsaggelos, A. K. (1997). Digital image restoration. IEEE signal processing magazine, 14(2), 24-41.
    [Google Scholar]
  2. Bao, W., Xu, B., & Chen, Z. (2019). Monofenet: Monocular 3d object detection with feature enhancement networks. IEEE Transactions on Image Processing, 29, 2753-2765.
    [Google Scholar]
  3. Barabas, I., Todoruţ, A., Cordoş, N., & Molea, A. (2017, October). Current challenges in autonomous driving. In IOP conference series: materials science and engineering (Vol. 252, No. 1, p. 012096). IOP Publishing.
    [Google Scholar]
  4. Li, J., Yang, B., Chen, C., Huang, R., Dong, Z., & Xiao, W. (2018). Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features. ISPRS Journal of Photogrammetry and Remote Sensing, 136, 41-57.
    [Google Scholar]
  5. Liao, Y., Li, J., Kang, S., Li, Q., Zhu, G., Yuan, S., ... & Yang, B. (2023). SE-Calib: Semantic Edge-Based LiDAR–Camera Boresight Online Calibration in Urban Scenes. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-13.
    [Google Scholar]
  6. Wang, J. G., & Zhou, L. B. (2018). Traffic light recognition with high dynamic range imaging and deep learning. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1341-1352.
    [Google Scholar]
  7. Melotti, G., Premebida, C., Gonçalves, N. M. D. S., Nunes, U. J., & Faria, D. R. (2018, November). Multimodal CNN pedestrian classification: a study on combining LIDAR and camera data. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 3138-3143). IEEE.
    [Google Scholar]
  8. Wang, K., Ma, S., Ren, F., & Lu, J. (2021). SBAS: Salient bundle adjustment for visual SLAM. IEEE Transactions on Instrumentation and Measurement, 70, 1-9.
    [Google Scholar]
  9. Kurihata, H., Takahashi, T., Ide, I., Mekada, Y., Murase, H., Tamatsu, Y., & Miyahara, T. (2005, June). Rainy weather recognition from in-vehicle camera images for driver assistance. In IEEE Proceedings. Intelligent Vehicles Symposium, 2005. (pp. 205-210). IEEE.
    [Google Scholar]
  10. Webster, D. D., & Breckon, T. P. (2015, September). Improved raindrop detection using combined shape and saliency descriptors with scene context isolation. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 4376-4380). IEEE.
    [Google Scholar]
  11. Zhang, W., Wang, Z., & Change Loy, C. Multi-modality cut and paste for 3d object detection. arXiv 2020. arXiv preprint arXiv:2012.12741.
    [Google Scholar]
  12. Filgueira, A., González-Jorge, H., Lagüela, S., Díaz-Vilariño, L., & Arias, P. (2017). Quantifying the influence of rain in LiDAR performance. Measurement, 95, 143-148.
    [Google Scholar]
  13. Rasshofer, R. H., Spies, M., & Spies, H. (2011). Influences of weather phenomena on automotive laser radar systems. Advances in radio science, 9, 49-60.
    [Google Scholar]
  14. Abro, G. E. M., Zulkifli, S. A. B., Kumar, K., El Ouanjli, N., Asirvadam, V. S., & Mossa, M. A. (2023). Comprehensive review of recent advancements in battery technology, propulsion, power interfaces, and vehicle network systems for intelligent autonomous and connected electric vehicles. Energies, 16(6), 2925.
    [Google Scholar]
  15. Feng, D., Haase-Schütz, C., Rosenbaum, L., Hertlein, H., Glaeser, C., Timm, F., ... & Dietmayer, K. (2020). Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1341-1360.
    [Google Scholar]
  16. Wei, Z., Zhang, F., Chang, S., Liu, Y., Wu, H., & Feng, Z. (2022). Mmwave radar and vision fusion for object detection in autonomous driving: A review. Sensors, 22(7), 2542.
    [Google Scholar]
  17. Svenningsson, P., Fioranelli, F., & Yarovoy, A. (2021, May). Radar-pointgnn: Graph based object recognition for unstructured radar point-cloud data. In 2021 IEEE Radar Conference (RadarConf21) (pp. 1-6). IEEE.
    [Google Scholar]
  18. Ulrich, M., Braun, S., Köhler, D., Niederlöhner, D., Faion, F., Gläser, C., & Blume, H. (2022, October). Improved orientation estimation and detection with hybrid object detection networks for automotive radar. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 111-117). IEEE.
    [Google Scholar]
  19. Kim, Y., Choi, J. W., & Kum, D. (2020, October). Grif net: Gated region of interest fusion network for robust 3d object detection from radar point cloud and monocular image. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 10857-10864). IEEE.
    [Google Scholar]
  20. Chadwick, S., Maddern, W., & Newman, P. (2019, May). Distant vehicle detection using radar and vision. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 8311-8317). IEEE.
    [Google Scholar]
  21. Nobis, F., Geisslinger, M., Weber, M., Betz, J., & Lienkamp, M. (2019, October). A deep learning-based radar and camera sensor fusion architecture for object detection. In 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) (pp. 1-7). IEEE.
    [Google Scholar]
  22. John, V., & Mita, S. (2019). RVNet: Deep sensor fusion of monocular camera and radar for image-based obstacle detection in challenging environments. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9 (pp. 351-364). Springer International Publishing.
    [Google Scholar]
  23. Li, L. Q., & Xie, Y. L. (2020, December). A feature pyramid fusion detection algorithm based on radar and camera sensor. In 2020 15th IEEE International Conference on Signal Processing (ICSP) (Vol. 1, pp. 366-370). IEEE.
    [Google Scholar]
  24. Chang, S., Zhang, Y., Zhang, F., Zhao, X., Huang, S., Feng, Z., & Wei, Z. (2020). Spatial attention fusion for obstacle detection using mmwave radar and vision sensor. Sensors, 20(4), 956.
    [Google Scholar]
  25. Nabati, R., & Qi, H. (2021). 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).
    [Google Scholar]
  26. Li, Y., Zeng, K., & Shen, T. (2023). CenterTransFuser: radar point cloud and visual information fusion for 3D object detection. EURASIP Journal on Advances in Signal Processing, 2023(1), 7.
    [Google Scholar]
  27. Long, Y., Kumar, A., Morris, D., Liu, X., Castro, M., & Chakravarty, P. (2023, June). RADIANT: Radar-image association network for 3D object detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 2, pp. 1808-1816).
    [Google Scholar]
  28. Li, P., Zhao, H., Liu, P., & Cao, F. (2020, August). Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving. In European Conference on Computer Vision (pp. 644-660). Cham: Springer International Publishing.
    [Google Scholar]
  29. Zhang, Y., Lu, J., & Zhou, J. (2021). Objects are different: Flexible monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3289-3298).
    [Google Scholar]
  30. Simonelli, A., Bulo, S. R., Porzi, L., López-Antequera, M., & Kontschieder, P. (2019). Disentangling monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1991-1999).
    [Google Scholar]
  31. Brazil, G., & Liu, X. (2019). M3d-rpn: Monocular 3d region proposal network for object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9287-9296).
    [Google Scholar]
  32. Cai, Y., Li, B., Jiao, Z., Li, H., Zeng, X., & Wang, X. (2020, April). Monocular 3d object detection with decoupled structured polygon estimation and height-guided depth estimation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 10478-10485).
    [Google Scholar]
  33. Chen, H., Huang, Y., Tian, W., Gao, Z., & Xiong, L. (2021). Monorun: Monocular 3d object detection by reconstruction and uncertainty propagation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10379-10388).
    [Google Scholar]
  34. Chen, Y., Tai, L., Sun, K., & Li, M. (2020). Monopair: Monocular 3d object detection using pairwise spatial relationships. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12093-12102).
    [Google Scholar]
  35. Heylen, J., De Wolf, M., Dawagne, B., Proesmans, M., Van Gool, L., Abbeloos, W., ... & Reino, D. O. (2021). Monocinis: Camera independent monocular 3d object detection using instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 923-934).
    [Google Scholar]
  36. Liu, Z., Wu, Z., & Tóth, R. (2020). Smoke: Single-stage monocular 3d object detection via keypoint estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 996-997).
    [Google Scholar]
  37. Liu, L., Lu, J., Xu, C., Tian, Q., & Zhou, J. (2019). Deep fitting degree scoring network for monocular 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1057-1066).
    [Google Scholar]
  38. Luo, S., Dai, H., Shao, L., & Ding, Y. (2021). M3dssd: Monocular 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6145-6154).
    [Google Scholar]
  39. Wang, T., Zhu, X., Pang, J., & Lin, D. (2021). Fcos3d: Fully convolutional one-stage monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 913-922).
    [Google Scholar]
  40. Lu, Y., Ma, X., Yang, L., Zhang, T., Liu, Y., Chu, Q., ... & Ouyang, W. (2021). Geometry uncertainty projection network for monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3111-3121).
    [Google Scholar]
  41. Mousavian, A., Anguelov, D., Flynn, J., & Kosecka, J. (2017). 3d bounding box estimation using deep learning and geometry. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 7074-7082).
    [Google Scholar]
  42. Brazil, G., Pons-Moll, G., Liu, X., & Schiele, B. (2020). Kinematic 3d object detection in monocular video. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII 16 (pp. 135-152). Springer International Publishing.
    [Google Scholar]
  43. Simonelli, A., Bulo, S. R., Porzi, L., Ricci, E., & Kontschieder, P. (2020). Towards generalization across depth for monocular 3d object detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16 (pp. 767-782). Springer International Publishing.
    [Google Scholar]
  44. Li, B., Ouyang, W., Sheng, L., Zeng, X., & Wang, X. (2019). Gs3d: An efficient 3d object detection framework for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1019-1028).
    [Google Scholar]
  45. Qin, Z., Wang, J., & Lu, Y. (2019, July). Monogrnet: A geometric reasoning network for monocular 3d object localization. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 8851-8858).
    [Google Scholar]
  46. Shi, X., Chen, Z., & Kim, T. K. (2020). Distance-normalized unified representation for monocular 3d object detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16 (pp. 91-107). Springer International Publishing.
    [Google Scholar]
  47. Hu, H. N., Cai, Q. Z., Wang, D., Lin, J., Sun, M., Krahenbuhl, P., ... & Yu, F. (2019). Joint monocular 3D vehicle detection and tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5390-5399).
    [Google Scholar]
  48. Ku, J., Pon, A. D., & Waslander, S. L. (2019). Monocular 3d object detection leveraging accurate proposals and shape reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11867-11876).
    [Google Scholar]
  49. Lian, Q., Ye, B., Xu, R., Yao, W., & Zhang, T. (2022). Exploring geometric consistency for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1685-1694).
    [Google Scholar]
  50. Zeeshan Zia, M., Stark, M., & Schindler, K. (2014). Are cars just 3d boxes?-jointly estimating the 3d shape of multiple objects. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3678-3685).
    [Google Scholar]
  51. Chabot, F., Chaouch, M., Rabarisoa, J., Teuliere, C., & Chateau, T. (2017). Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2040-2049).
    [Google Scholar]
  52. He, T., & Soatto, S. (2019, July). Mono3d++: Monocular 3d vehicle detection with two-scale 3d hypotheses and task priors. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 8409-8416).
    [Google Scholar]
  53. Rogage, K., & Doukari, O. (2024). 3D object recognition using deep learning for automatically generating semantic BIM data. Automation in Construction, 162, 105366.
    [Google Scholar]
  54. Manhardt, F., Kehl, W., & Gaidon, A. (2019). Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2069-2078).
    [Google Scholar]
  55. Beker, D., Kato, H., Morariu, M. A., Ando, T., Matsuoka, T., Kehl, W., & Gaidon, A. (2020). Monocular differentiable rendering for self-supervised 3d object detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 (pp. 514-529). Springer International Publishing.
    [Google Scholar]
  56. Zakharov, S., Kehl, W., Bhargava, A., & Gaidon, A. (2020). Autolabeling 3d objects with differentiable rendering of sdf shape priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12224-12233).
    [Google Scholar]
  57. Jörgensen, E., Zach, C., & Kahl, F. (2019). Monocular 3d object detection and box fitting trained end-to-end using intersection-over-union loss. arXiv preprint arXiv:1906.08070.
    [Google Scholar]
  58. Naiden, A., Paunescu, V., Kim, G., Jeon, B., & Leordeanu, M. (2019, September). Shift r-cnn: Deep monocular 3d object detection with closed-form geometric constraints. In 2019 IEEE international conference on image processing (ICIP) (pp. 61-65). IEEE.
    [Google Scholar]
  59. Shi, X., Ye, Q., Chen, X., Chen, C., Chen, Z., & Kim, T. K. (2021). Geometry-based distance decomposition for monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15172-15181).
    [Google Scholar]
  60. Wang, T., Pang, J., & Lin, D. (2022, October). Monocular 3d object detection with depth from motion. In European Conference on Computer Vision (pp. 386-403). Cham: Springer Nature Switzerland.
    [Google Scholar]
  61. Wang, Y., Chao, W. L., Garg, D., Hariharan, B., Campbell, M., & Weinberger, K. Q. (2019). Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8445-8453).
    [Google Scholar]
  62. You, Y., Wang, Y., Chao, W. L., Garg, D., Pleiss, G., Hariharan, B., ... & Weinberger, K. Q. (2019). Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving. arXiv preprint arXiv:1906.06310.
    [Google Scholar]
  63. Ding, M., Huo, Y., Yi, H., Wang, Z., Shi, J., Lu, Z., & Luo, P. (2020). Learning depth-guided convolutions for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition workshops (pp. 1000-1001).
    [Google Scholar]
  64. Weng, X., & Kitani, K. (2019). Monocular 3d object detection with pseudo-lidar point cloud. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (pp. 0-0).
    [Google Scholar]
  65. Wang, L., Du, L., Ye, X., Fu, Y., Guo, G., Xue, X., ... & Zhang, L. (2021). Depth-conditioned dynamic message propagation for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 454-463).
    [Google Scholar]
  66. Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., & Fan, X. (2019). Accurate monocular 3d object detection via color-embedded 3d reconstruction for autonomous driving. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6851-6860).
    [Google Scholar]
  67. Park, D., Ambrus, R., Guizilini, V., Li, J., & Gaidon, A. (2021). Is pseudo-lidar needed for monocular 3d object detection?. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3142-3152).
    [Google Scholar]
  68. Ma, X., Liu, S., Xia, Z., Zhang, H., Zeng, X., & Ouyang, W. (2020). Rethinking pseudo-lidar representation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII 16 (pp. 311-327). Springer International Publishing.
    [Google Scholar]
  69. Chang, J., & Wetzstein, G. (2019). Deep optics for monocular depth estimation and 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10193-10202).
    [Google Scholar]
  70. Li, P., Chen, X., & Shen, S. (2019). Stereo r-cnn based 3d object detection for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7644-7652).
    [Google Scholar]
  71. Sun, J., Chen, L., Xie, Y., Zhang, S., Jiang, Q., Zhou, X., & Bao, H. (2020). Disp r-cnn: Stereo 3d object detection via shape prior guided instance disparity estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10548-10557).
    [Google Scholar]
  72. Liu, Y., Wang, L., & Liu, M. (2021, May). Yolostereo3d: A step back to 2d for efficient stereo 3d detection. In 2021 IEEE international conference on Robotics and automation (ICRA) (pp. 13018-13024). IEEE.
    [Google Scholar]
  73. Qin, Z., Wang, J., & Lu, Y. (2019). Triangulation learning network: from monocular to stereo 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7615-7623).
    [Google Scholar]
  74. Chen, Y., Liu, S., Shen, X., & Jia, J. (2020). Dsgn: Deep stereo geometry network for 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12536-12545).
    [Google Scholar]
  75. Guo, X., Shi, S., Wang, X., & Li, H. (2021). Liga-stereo: Learning lidar geometry aware representations for stereo-based 3d detector. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3153-3163).
    [Google Scholar]
  76. Guo, X., Wang, S. S. X., & Li, H. Supplementary Materials of LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector.
    [Google Scholar]
  77. Su, K., Yan, W., Wei, X., & Gu, M. (2022). Stereo VoVNet-CNN for 3D object detection. Multimedia Tools and Applications, 81(25), 35803-35813.
    [Google Scholar]
  78. Xu, Z., Zhang, W., Ye, X., Tan, X., Yang, W., Wen, S., ... & Huang, L. (2020, April). Zoomnet: Part-aware adaptive zooming neural network for 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 12557-12564).
    [Google Scholar]
  79. Shi, Y., Guo, Y., Mi, Z., & Li, X. (2022). Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing, 471, 219-229.
    [Google Scholar]
  80. Chen, L., Sun, J., Xie, Y., Zhang, S., Shuai, Q., Jiang, Q., ... & Zhou, X. (2021). Shape prior guided instance disparity estimation for 3d object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 5529-5540.
    [Google Scholar]
  81. Peng, W., Pan, H., Liu, H., & Sun, Y. (2020). Ida-3d: Instance-depth-aware 3d object detection from stereo vision for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13015-13024).
    [Google Scholar]
  82. Peng, X., Zhu, X., Wang, T., & Ma, Y. (2022). Side: Center-based stereo 3d detector with structure-aware instance depth estimation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 119-128).
    [Google Scholar]
  83. Qian, R., Garg, D., Wang, Y., You, Y., Belongie, S., Hariharan, B., ... & Chao, W. L. (2020). End-to-end pseudo-lidar for image-based 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5881-5890).
    [Google Scholar]
  84. Liu, Y., Yixuan, Y., & Liu, M. (2021). Ground-aware monocular 3d object detection for autonomous driving. IEEE Robotics and Automation Letters, 6(2), 919-926.
    [Google Scholar]
  85. Peng, L., Liu, F., Yu, Z., Yan, S., Deng, D., Yang, Z., ... & Cai, D. (2022, October). Lidar point cloud guided monocular 3d object detection. In European conference on computer vision (pp. 123-139). Cham: Springer Nature Switzerland.
    [Google Scholar]
  86. Wang, X., Yin, W., Kong, T., Jiang, Y., Li, L., & Shen, C. (2020, April). Task-aware monocular depth estimation for 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 12257-12264).
    [Google Scholar]
  87. Ye, X., Du, L., Shi, Y., Li, Y., Tan, X., Feng, J., ... & Wen, S. (2020). Monocular 3d object detection via feature domain adaptation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16 (pp. 17-34). Springer International Publishing.
    [Google Scholar]
  88. Wang, L., Zhang, L., Zhu, Y., Zhang, Z., He, T., Li, M., & Xue, X. (2021). Progressive coordinate transforms for monocular 3d object detection. Advances in Neural Information Processing Systems, 34, 13364-13377.
    [Google Scholar]
  89. Meng, H., Li, C., Chen, G., & Chen, L. (2023). Efficient 3D Object Detection Based on Pseudo-LiDAR Representation. IEEE Transactions on Intelligent Vehicles.
    [Google Scholar]
  90. Tao, C., Cao, C., Cheng, H., Gao, Z., Luo, X., Zhang, Z., & Zheng, S. (2023). An efficient 3D object detection method based on fast guided anchor stereo RCNN. Advanced Engineering Informatics, 57, 102069.
    [Google Scholar]
  91. Xia, Y., Shi, L., Ding, Z., Henriques, J. F., & Cremers, D. (2024). Text2loc: 3d point cloud localization from natural language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14958-14967).
    [Google Scholar]
  92. Königshof, H., Salscheider, N. O., & Stiller, C. (2019, October). Realtime 3d object detection for automated driving using stereo vision and semantic information. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 1405-1410). IEEE.
    [Google Scholar]
  93. Tao, C., He, H., Xu, F., & Cao, J. (2021). Stereo priori RCNN based car detection on point level for autonomous driving. Knowledge-Based Systems, 229, 107346.
    [Google Scholar]
  94. Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017). Multi-view 3d object detection network for autonomous driving. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 1907-1915).
    [Google Scholar]
  95. Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T., ... & Vasudevan, V. (2020, May). End-to-end multi-view fusion for 3d object detection in lidar point clouds. In Conference on Robot Learning (pp. 923-932). PMLR.
    [Google Scholar]
  96. Rubino, C., Crocco, M., & Del Bue, A. (2017). 3d object localisation from multi-view image detections. IEEE transactions on pattern analysis and machine intelligence, 40(6), 1281-1294.
    [Google Scholar]
  97. Yang, Z., & Wang, L. (2019). Learning relationships for multi-view 3D object recognition. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 7505-7514).
    [Google Scholar]
  98. Wang, C., Pelillo, M., & Siddiqi, K. (2019). Dominant set clustering and pooling for multi-view 3d object recognition. arXiv preprint arXiv:1906.01592.
    [Google Scholar]
  99. Deng, J., & Czarnecki, K. (2019, October). MLOD: A multi-view 3D object detection based on robust feature fusion method. In 2019 IEEE intelligent transportation systems conference (ITSC) (pp. 279-284). IEEE.
    [Google Scholar]
  100. Choy, C. B., Xu, D., Gwak, J., Chen, K., & Savarese, S. (2016). 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14 (pp. 628-644). Springer International Publishing.
    [Google Scholar]
  101. Ku, J., Pon, A. D., Walsh, S., & Waslander, S. L. (2019, November). Improving 3d object detection for pedestrians with virtual multi-view synthesis orientation estimation. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3459-3466). IEEE.
    [Google Scholar]
  102. Hong, C., Yu, J., You, J., Chen, X., & Tao, D. (2015). Multi-view ensemble manifold regularization for 3D object recognition. Information sciences, 320, 395-405.
    [Google Scholar]
  103. Philion, J., & Fidler, S. (2020). 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 International Publishing.
    [Google Scholar]
  104. Wang, Y., Guizilini, V. C., Zhang, T., Wang, Y., Zhao, H., & Solomon, J. (2022, January). Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning (pp. 180-191). PMLR.
    [Google Scholar]
  105. Lin, J., Rickert, M., & Knoll, A. (2021, May). Deep hierarchical rotation invariance learning with exact geometry feature representation for point cloud classification. In 2021 IEEE international conference on robotics and automation (ICRA) (pp. 9529-9535). IEEE.
    [Google Scholar]
  106. Zhang, K., Hao, M., Wang, J., Chen, X., Leng, Y., de Silva, C. W., & Fu, C. (2021, November). Linked dynamic graph cnn: Learning through point cloud by linking hierarchical features. In 2021 27th international conference on mechatronics and machine vision in practice (M2VIP) (pp. 7-12). IEEE.
    [Google Scholar]
  107. Zhang, J., Liu, J., Liu, X., Wei, J., Cao, J., & Tang, K. (2021). Feature interpolation convolution for point cloud analysis. Computers & Graphics, 99, 182-191.
    [Google Scholar]
  108. Shi, S., Wang, X., & Li, H. (2019). Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 770-779).
    [Google Scholar]
  109. Liu, Z., Tang, H., Lin, Y., & Han, S. (2019). Point-voxel cnn for efficient 3d deep learning. Advances in neural information processing systems, 32.
    [Google Scholar]
  110. Chen, C., Chen, Z., Zhang, J., & Tao, D. (2022, June). Sasa: Semantics-augmented set abstraction for point-based 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 1, pp. 221-229).
    [Google Scholar]
  111. Ngiam, J., Caine, B., Han, W., Yang, B., Chai, Y., Sun, P., ... & Vasudevan, V. (2019). Starnet: Targeted computation for object detection in point clouds. arXiv preprint arXiv:1908.11069.
    [Google Scholar]
  112. Yang, Z., Sun, Y., Liu, S., & Jia, J. (2020). 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11040-11048).
    [Google Scholar]
  113. Yang, H., Liu, Z., Wu, X., Wang, W., Qian, W., He, X., & Cai, D. (2022, October). Graph r-cnn: Towards accurate 3d object detection with semantic-decorated local graph. In European Conference on Computer Vision (pp. 662-679). Cham: Springer Nature Switzerland.
    [Google Scholar]
  114. NShi, W., & Rajkumar, R. (2020). Point-gnn: Graph neural network for 3d object detection in a point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1711-1719).
    [Google Scholar]
  115. Zhou, D., Fang, J., Song, X., Liu, L., Yin, J., Dai, Y., ... & Yang, R. (2020). Joint 3d instance segmentation and object detection for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1839-1849).
    [Google Scholar]
  116. He, Q., Wang, Z., Zeng, H., Zeng, Y., & Liu, Y. (2022, June). Svga-net: Sparse voxel-graph attention network for 3d object detection from point clouds. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 1, pp. 870-878).
    [Google Scholar]
  117. Zarzar, J., Giancola, S., & Ghanem, B. (2019). PointRGCN: Graph convolution networks for 3D vehicles detection refinement. arXiv preprint arXiv:1911.12236.
    [Google Scholar]
  118. Feng, M., Gilani, S. Z., Wang, Y., Zhang, L., & Mian, A. (2020). Relation graph network for 3D object detection in point clouds. IEEE Transactions on Image Processing, 30, 92-107.
    [Google Scholar]
  119. Pan, X., Xia, Z., Song, S., Li, L. E., & Huang, G. (2021). 3d object detection with pointformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7463-7472).
    [Google Scholar]
  120. Liu, Z., Zhang, Z., Cao, Y., Hu, H., & Tong, X. (2021). Group-free 3d object detection via transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2949-2958).
    [Google Scholar]
  121. Fayyad, J., Jaradat, M. A., Gruyer, D., & Najjaran, H. (2020). Deep learning sensor fusion for autonomous vehicle perception and localization: A review. Sensors, 20(15), 4220.
    [Google Scholar]
  122. Wang, Q., Chen, J., Deng, J., & Zhang, X. (2021). 3D-CenterNet: 3D object detection network for point clouds with center estimation priority. Pattern Recognition, 115, 107884.
    [Google Scholar]
  123. Wang, D. Z., & Posner, I. (2015, July). Voting for voting in online point cloud object detection. In Robotics: science and systems (Vol. 1, No. 3, pp. 10-15).
    [Google Scholar]
  124. Engelcke, M., Rao, D., Wang, D. Z., Tong, C. H., & Posner, I. (2017, May). Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1355-1361). IEEE.
    [Google Scholar]
  125. Cui, Y., Zhang, Y., Dong, J., Sun, H., Chen, X., & Zhu, F. (2024). Link3d: Linear keypoints representation for 3d lidar point cloud. IEEE Robotics and Automation Letters.
    [Google Scholar]
  126. Bai, L., Li, Y., Cen, M., & Hu, F. (2021). 3D instance segmentation and object detection framework based on the fusion of LIDAR remote sensing and optical image sensing. Remote Sensing, 13(16), 3288.
    [Google Scholar]
  127. Wang, B., Zhu, M., Lu, Y., Wang, J., Gao, W., & Wei, H. (2021). Real-time 3D object detection from point cloud through foreground segmentation. IEEE Access, 9, 84886-84898.
    [Google Scholar]
  128. Yang, B., Liang, M., & Urtasun, R. (2018, October). Hdnet: Exploiting hd maps for 3d object detection. In Conference on Robot Learning (pp. 146-155). PMLR.
    [Google Scholar]
  129. Zhou, Y., & Tuzel, O. (2018). Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4490-4499).
    [Google Scholar]
  130. Yan, Y., Mao, Y., & Li, B. (2018). Second: Sparsely embedded convolutional detection. Sensors, 18(10), 3337.
    [Google Scholar]
  131. Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019). Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12697-12705).
    [Google Scholar]
  132. Wang, Y., Fathi, A., Kundu, A., Ross, D. A., Pantofaru, C., Funkhouser, T., & Solomon, J. (2020). Pillar-based object detection for autonomous driving. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16 (pp. 18-34). Springer International Publishing.
    [Google Scholar]
  133. Shi, S., Wang, Z., Shi, J., Wang, X., & Li, H. (2020). From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network. IEEE transactions on pattern analysis and machine intelligence, 43(8), 2647-2664.
    [Google Scholar]
  134. Li, B. (2017, September). 3d fully convolutional network for vehicle detection in point cloud. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1513-1518). IEEE.
    [Google Scholar]
  135. Yin, T., Zhou, X., & Krahenbuhl, P. (2021). Center-based 3d object detection and tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11784-11793).
    [Google Scholar]
  136. Mao, J., Xue, Y., Niu, M., Bai, H., Feng, J., Liang, X., ... & Xu, C. (2021). Voxel transformer for 3d object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3164-3173).
    [Google Scholar]
  137. Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., & Li, H. (2021, May). Voxel r-cnn: Towards high performance voxel-based 3d object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 2, pp. 1201-1209).
    [Google Scholar]
  138. Song, Z., Wei, H., Jia, C., Xia, Y., Li, X., & Zhang, C. (2023). VP-Net: Voxels as points for 3-D object detection. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-12.
    [Google Scholar]
  139. Wang, H., Chen, Z., Cai, Y., Chen, L., Li, Y., Sotelo, M. A., & Li, Z. (2022). Voxel-RCNN-complex: An effective 3-D point cloud object detector for complex traffic conditions. IEEE Transactions on Instrumentation and Measurement, 71, 1-12.
    [Google Scholar]
  140. Sheng, H., Cai, S., Liu, Y., Deng, B., Huang, J., Hua, X. S., & Zhao, M. J. (2021). Improving 3d object detection with channel-wise transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 2743-2752).
    [Google Scholar]
  141. Li, J., Dai, H., Shao, L., & Ding, Y. (2021, October). From voxel to point: IoU-guided 3D object detection for point cloud with voxel-to-point decoder. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 4622-4631).
    [Google Scholar]
  142. Miao, Z., Chen, J., Pan, H., Zhang, R., Liu, K., Hao, P., ... & Zhan, X. (2021). Pvgnet: A bottom-up one-stage 3d object detector with integrated multi-level features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3279-3288).
    [Google Scholar]
  143. Noh, J., Lee, S., & Ham, B. (2021). Hvpr: Hybrid voxel-point representation for single-stage 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 14605-14614).
    [Google Scholar]
  144. Guan, T., Wang, J., Lan, S., Chandra, R., Wu, Z., Davis, L., & Manocha, D. (2022). M3detr: Multi-representation, multi-scale, mutual-relation 3d object detection with transformers. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 772-782).
    [Google Scholar]
  145. Mao, J., Niu, M., Bai, H., Liang, X., Xu, H., & Xu, C. (2021). Pyramid r-cnn: Towards better performance and adaptability for 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2723-2732).
    [Google Scholar]
  146. Liu, Z., Tang, H., Zhao, S., Shao, K., & Han, S. (2021). Pvnas: 3d neural architecture search with point-voxel convolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 8552-8568.
    [Google Scholar]
  147. Li, P., Su, S., & Zhao, H. (2021, May). Rts3d: Real-time stereo 3d detection from 4d feature-consistency embedding space for autonomous driving. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 3, pp. 1930-1939).
    [Google Scholar]
  148. Zhang, R., Qiu, H., Wang, T., Guo, Z., Cui, Z., Qiao, Y., ... & Gao, P. (2023). MonoDETR: Depth-guided transformer for monocular 3D object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9155-9166).
    [Google Scholar]
  149. Lu, B., Sun, Y., & Yang, Z. (2023). Voxel Graph Attention for 3D Object Detection from Point Clouds. IEEE Transactions on Instrumentation and Measurement.
    [Google Scholar]
  150. Deng, J., Zhou, W., Zhang, Y., & Li, H. (2021). From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection. IEEE Transactions on Circuits and Systems for Video Technology, 31(12), 4722-4734.
    [Google Scholar]
  151. Zhang, Y., Chen, J., & Huang, D. (2022). Cat-det: Contrastively augmented transformer for multi-modal 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 908-917).
    [Google Scholar]
  152. Shi, S., Jiang, L., Deng, J., Wang, Z., Guo, C., Shi, J., ... & Li, H. (2023). PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3D object detection. International Journal of Computer Vision, 131(2), 531-551.
    [Google Scholar]
  153. Wu, P., Gu, L., Yan, X., Xie, H., Wang, F. L., Cheng, G., & Wei, M. (2023). PV-RCNN++: semantical point-voxel feature interaction for 3D object detection. The Visual Computer, 39(6), 2425-2440.
    [Google Scholar]
  154. Tu, J., Wang, P., & Liu, F. (2021, July). Pp-rcnn: Point-pillars feature set abstraction for 3d real-time object detection. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
    [Google Scholar]
  155. Li, J., Luo, C., & Yang, X. (2023). PillarNeXt: Rethinking network designs for 3D object detection in LiDAR point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17567-17576).
    [Google Scholar]
  156. Hu, J. S., Kuai, T., & Waslander, S. L. (2022). Point density-aware voxels for lidar 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8469-8478).
    [Google Scholar]
  157. Geng, K., Dong, G., Yin, G., & Hu, J. (2020). Deep dual-modal traffic objects instance segmentation method using camera and lidar data for autonomous driving. Remote Sensing, 12(20), 3274.
    [Google Scholar]
  158. Ignatious, H. A., & Khan, M. (2022). An overview of sensors in Autonomous Vehicles. Procedia Computer Science, 198, 736-741.
    [Google Scholar]
  159. Vargas, J., Alsweiss, S., Toker, O., Razdan, R., & Santos, J. (2021). An overview of autonomous vehicles sensors and their vulnerability to weather conditions. Sensors, 21(16), 5397.
    [Google Scholar]
  160. Cartenì, A. (2020). The acceptability value of autonomous vehicles: A quantitative analysis of the willingness to pay for shared autonomous vehicles (SAVs) mobility services. Transportation Research Interdisciplinary Perspectives, 8, 100224.
    [Google Scholar]
  161. Sakib, S. M. (2022). LiDAR Technology-An Overview. IUP Journal of Electrical & Electronics Engineering, 15(1).
    [Google Scholar]
  162. Bastos, D., Monteiro, P. P., Oliveira, A. S., & Drummond, M. V. (2021, February). An overview of LiDAR requirements and techniques for autonomous driving. In 2021 Telecoms Conference (ConfTELE) (pp. 1-6). IEEE.
    [Google Scholar]
  163. Royo, S., & Ballesta, M. (2019). An overview of imaging lidar sensors for autonomous vehicles.
    [Google Scholar]
  164. Thomä, R., Dallmann, T., Jovanoska, S., Knott, P., & Schmeink, A. (2021, March). Joint communication and radar sensing: An overview. In 2021 15th European Conference on Antennas and Propagation (EuCAP) (pp. 1-5). IEEE.
    [Google Scholar]
  165. Paterniani, G., Sgreccia, D., Davoli, A., Guerzoni, G., Di Viesti, P., Valenti, A. C., ... & Boriani, G. (2023). Radar-based monitoring of vital signs: A tutorial overview. Proceedings of the IEEE, 111(3), 277-317.
    [Google Scholar]
  166. Mielle, M., Magnusson, M., & Lilienthal, A. J. (2019, September). A comparative analysis of radar and lidar sensing for localization and mapping. In 2019 European Conference on Mobile Robots (ECMR) (pp. 1-6). IEEE.
    [Google Scholar]
  167. Kim, K. E., Lee, C. J., Pae, D. S., & Lim, M. T. (2017, October). Sensor fusion for vehicle tracking with camera and radar sensor. In 2017 17th International Conference on Control, Automation and Systems (ICCAS) (pp. 1075-1077). IEEE.
    [Google Scholar]
  168. Abro, G. E. M., Abdullahi, M. S., Ganasan, J., & Ricky, S. K. (2021). Prototyping an IoT-enabled Autonomous Unmanned Ground Vehicle Using SLAM. International Journal of Control Systems and Robotics, 6.
    [Google Scholar]
  169. Pravallika, A., Hashmi, M. F., & Gupta, A. (2024). Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving. IEEE Access.
    [Google Scholar]
  170. Berrio, J. S., Shan, M., Worrall, S., & Nebot, E. (2021). Camera-LIDAR integration: Probabilistic sensor fusion for semantic mapping. IEEE Transactions on Intelligent Transportation Systems, 23(7), 7637-7652.
    [Google Scholar]
  171. Khan, D., Baek, M., Kim, M. Y., & Han, D. S. (2022, October). Multimodal Object Detection and Ranging Based on Camera and Lidar Sensor Fusion for Autonomous Driving. In 2022 27th Asia Pacific Conference on Communications (APCC) (pp. 342-343). IEEE.
    [Google Scholar]
  172. Das, D., Adhikary, N., & Chaudhury, S. (2022, September). Sensor fusion in autonomous vehicle using LiDAR and camera Sensor. In 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 336-341). IEEE.
    [Google Scholar]
  173. Mendez, J., Molina, M., Rodriguez, N., Cuellar, M. P., & Morales, D. P. (2021). Camera-LiDAR multi-level sensor fusion for target detection at the network edge. Sensors, 21(12), 3992.
    [Google Scholar]
  174. Thakur, A., & Rajalakshmi, P. (2023, July). LiDAR and Camera Raw Data Sensor Fusion in Real-Time for Obstacle Detection. In 2023 IEEE Sensors Applications Symposium (SAS) (pp. 1-6). IEEE.
    [Google Scholar]
  175. Ai, C., Qi, Z., Zheng, L., Geng, D., Feng, Z., & Sun, X. (2021, March). Research on mapping method based on data fusion of lidar and depth camera. In 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) (pp. 360-365). IEEE.
    [Google Scholar]
Cite This Article
APA Style
Abro, G. E. M., Ali, Z. A., & Rajput, S. (2024). Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions. ICCK Transactions on Sensing, Communication, and Control, 1(1), 3–29. https://doi.org/10.62762/TSCC.2024.989358
Export Citation
RIS Format
Compatible with EndNote, Zotero, Mendeley, and other reference managers
RIS format data for reference managers
TY  - JOUR
AU  - Abro, Ghulam E Mustafa
AU  - Ali, Zain Anwar
AU  - Rajput, Summaiya
PY  - 2024
DA  - 2024/10/12
TI  - Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 1
IS  - 1
SP  - 3
EP  - 29
DO  - 10.62762/TSCC.2024.989358
UR  - https://www.icck.org/article/abs/TSCC.2024.989358
KW  - autonomous systems
KW  - camera
KW  - fusion methods
KW  - LiDAR
KW  - object detection
KW  - radar and three-dimensional
AB  - This review paper offers a thorough assessment of three-dimensional object recognition methods, an essential element in the perception frameworks of autonomous systems. This analysis emphasises the integration of LiDAR and camera sensors, providing a distinctive contrast with more economical alternatives like camera-only or camera-Radar combinations. This study objectively evaluates performance and practical implementation issues, such as cost and operational efficiency, thereby elucidating the limitations of existing systems and proposing avenues for further research. The insights provided render it a significant asset for enhancing 3D object recognition and autonomy in intelligent systems.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
BibTeX format data for LaTeX and reference managers
@article{Abro2024Innovation,
  author = {Ghulam E Mustafa Abro and Zain Anwar Ali and Summaiya Rajput},
  title = {Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2024},
  volume = {1},
  number = {1},
  pages = {3-29},
  doi = {10.62762/TSCC.2024.989358},
  url = {https://www.icck.org/article/abs/TSCC.2024.989358},
  abstract = {This review paper offers a thorough assessment of three-dimensional object recognition methods, an essential element in the perception frameworks of autonomous systems. This analysis emphasises the integration of LiDAR and camera sensors, providing a distinctive contrast with more economical alternatives like camera-only or camera-Radar combinations. This study objectively evaluates performance and practical implementation issues, such as cost and operational efficiency, thereby elucidating the limitations of existing systems and proposing avenues for further research. The insights provided render it a significant asset for enhancing 3D object recognition and autonomy in intelligent systems.},
  keywords = {autonomous systems, camera, fusion methods, LiDAR, object detection, radar and three-dimensional},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}
Article Metrics
Citations:

Google Scholar
14

Crossref

10

Scopus

13

Web of Science

12
Article Access Statistics:
Views: 6130
PDF Downloads: 488
Publisher's Note
ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and Permissions
Institute of Central Computation and Knowledge (ICCK) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ICCK Transactions on Sensing, Communication, and Control

ICCK Transactions on Sensing, Communication, and Control

ISSN: 3068-9287 (Online) | ISSN: 3068-9279 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/icck/