Volume 14 Issue 3
Jun.  2025
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SONG Shaojing, LI Xinjian, and FANG Feiyi. A review of point cloud fusion technology: Methods, applications, and challenges[J]. Journal of Radars, 2025, 14(3): 528–547. doi: 10.12000/JR24236
Citation: SONG Shaojing, LI Xinjian, and FANG Feiyi. A review of point cloud fusion technology: Methods, applications, and challenges[J]. Journal of Radars, 2025, 14(3): 528–547. doi: 10.12000/JR24236

A Review of Point Cloud Fusion Technology: Methods, Applications, and Challenges

DOI: 10.12000/JR24236 CSTR: 32380.14.JR24236
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  • Corresponding author: FANG Feiyi, fyfang@sspu.edu.cn
  • Received Date: 2024-11-24
  • Rev Recd Date: 2025-06-03
  • Available Online: 2025-06-06
  • Publish Date: 2025-06-13
  • As an important method of 3D (Three-Dimensional) data processing, point cloud fusion technology has shown great potential and promising applications in many fields. This paper systematically reviews the basic concepts, commonly used techniques, and applications of point cloud fusion and thoroughly analyzes the current status and future development trends of various fusion methods. Additionally, the paper explores the practical applications and challenges of point cloud fusion in fields such as autonomous driving, architecture, and robotics. Special attention is given to balancing algorithmic complexity with fusion accuracy, particularly in addressing issues like noise, data sparsity, and uneven point cloud density. This study serves as a strong reference for the future development of point cloud fusion technology by providing a comprehensive overview of the existing research progress and identifying possible research directions for further improving the accuracy, robustness, and efficiency of fusion algorithms.

     

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  • [1]
    ABBASI R, BASHIR A K, ALYAMANI H J, et al. Lidar point cloud compression, processing and learning for autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(1): 962–979. doi: 10.1109/TITS.2022.3167957.
    [2]
    QURESHI A H, ALALOUL W S, MURTIYOSO A, et al. Smart rebar progress monitoring using 3D point cloud model[J]. Expert Systems with Applications, 2024, 249: 123562. doi: 10.1016/j.eswa.2024.123562.
    [3]
    PETKOV P. Object detection and recognition with PointPillars in LiDAR point clouds-comparisions[R]. NSC-614-5849, 2024.
    [4]
    龚靖渝, 楼雨京, 柳奉奇, 等. 三维场景点云理解与重建技术[J]. 中国图象图形学报, 2023, 28(6): 1741–1766. doi: 10.11834/jig.230004.

    GONG Jingyu, LOU Yujing, LIU Fengqi, et al. Scene point cloud understanding and reconstruction technologies in 3D space[J]. Journal of Image and Graphics, 2023, 28(6): 1741–1766. doi: 10.11834/jig.230004.
    [5]
    ZHANG Bo, WANG Haosen, YOU Suilian, et al. A small-size 3D object detection network for analyzing the sparsity of raw LiDAR point cloud[J]. Journal of Russian Laser Research, 2023, 44(6): 646–655. doi: 10.1007/s10946-023-10173-3.
    [6]
    LI Shaorui, ZHU Zhenchang, DENG Weitang, et al. Estimation of aboveground biomass of different vegetation types in mangrove forests based on UAV remote sensing[J]. Sustainable Horizons, 2024, 11: 100100. doi: 10.1016/j.horiz.2024.100100.
    [7]
    余杭. 基于激光雷达的3D目标检测研究综述[J]. 汽车文摘, 2024(2): 18–27. doi: 10.19822/j.cnki.1671-6329.20230082.

    YU Hang. A review on LiDAR-based 3D target detection research[J]. Automotive Digest, 2024(2): 18–27. doi: 10.19822/j.cnki.1671-6329.20230082.
    [8]
    郭迟, 刘阳, 罗亚荣, 等. 图像语义信息在视觉SLAM中的应用研究进展[J]. 测绘学报, 2024, 53(6): 1057–1076. doi: 10.11947/j.AGCS.2024.20230259.

    GUO Chi, LIU Yang, LUO Yarong, et al. Research progress in the application of image semantic information in visual SLAM[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1057–1076. doi: 10.11947/j.AGCS.2024.20230259.
    [9]
    ZHANG L, VAN OOSTEROM P, and LIU H. Visualization of point cloud models in mobile augmented reality using continuous level of detail method[C]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, London, UK, 2020: 167–170. doi: 10.5194/isprs-archives-XLIV-4-W1-2020-167-2020.
    [10]
    POLIYAPRAM V, WANG Weimin, and NAKAMURA R. A point-wise LiDAR and image multimodal fusion network (PMNet) for aerial point cloud 3D semantic segmentation[J]. Remote Sensing, 2019, 11(24): 2961. doi: 10.3390/rs11242961.
    [11]
    ZENG Tianjiao, ZHANG Wensi, ZHAN Xu, et al. A novel multimodal fusion framework based on point cloud registration for near-field 3D SAR perception[J]. Remote Sensing, 2024, 16(6): 952. doi: 10.3390/rs16060952.
    [12]
    CRISAN A, PEPE M, COSTANTINO D, et al. From 3D point cloud to an intelligent model set for cultural heritage conservation[J]. Heritage, 2024, 7(3): 1419–1437. doi: 10.3390/heritage7030068.
    [13]
    KIVILCIM C Ö and DURAN Z. A semi-automated point cloud processing methodology for 3D cultural heritage documentation[C]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 2016: 293–296. doi: 10.5194/isprs-archives-XLI-B5-293-2016.
    [14]
    WANG Yong, ZHOU Pengbo, GENG Guohua, et al. Enhancing point cloud registration with transformer: Cultural heritage protection of the Terracotta Warriors[J]. Heritage Science, 2024, 12(1): 314. doi: 10.1186/s40494-024-01425-9.
    [15]
    YANG Su, HOU Miaole, and LI Songnian. Three-dimensional point cloud semantic segmentation for cultural heritage: A comprehensive review[J]. Remote Sensing, 2023, 15(3): 548. doi: 10.3390/rs15030548.
    [16]
    李佳益, 马智亮, 陈礼杰, 等. 面向施工机器人定位的多模态数据融合方法研究综述[J]. 计算机工程与应用, 2024, 60(15): 11–23. doi: 10.3778/j.issn.1002-8331.2402-0008.

    LI Jiayi, MA Zhiliang, CHEN Lijie, et al. Comprehensive review of multimodal data fusion methods for construction robot localization[J]. Computer Engineering and Applications, 2024, 60(15): 11–23. doi: 10.3778/j.issn.1002-8331.2402-0008.
    [17]
    周燕, 李文俊, 党兆龙, 等. 深度学习的三维模型识别研究综述[J]. 计算机科学与探索, 2024, 18(4): 916–929. doi: 10.3778/j.issn.1673-9418.2309010.

    ZHOU Yan, LI Wenjun, DANG Zhaolong, et al. Survey of 3D model recognition based on deep learning[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(4): 916–929. doi: 10.3778/j.issn.1673-9418.2309010.
    [18]
    WANG Zhengren. 3D representation methods: A survey[J]. arXiv preprint arXiv, 2410.06475, 2024.
    [19]
    ZHOU Yin, SUN Pei, ZHANG Yu, et al. End-to-end multi-view fusion for 3D object detection in LiDAR point clouds[C]. The 3rd Annual Conference on Robot Learning, Osaka, Japan, 2019: 923–932.
    [20]
    陈慧娴, 吴一全, 张耀. 基于深度学习的三维点云分析方法研究进展[J]. 仪器仪表学报, 2023, 44(11): 130–158. doi: 10.19650/j.cnki.cjsi.J2311134.

    CHEN Huixian, WU Yiquan, and ZHANG Yao. Research progress of 3D point cloud analysis methods based on deep learning[J]. Chinese Journal of Scientific Instrument, 2023, 44(11): 130–158. doi: 10.19650/j.cnki.cjsi.J2311134.
    [21]
    LUO Haojun and WEN C Y. A low-cost relative positioning method for UAV/UGV coordinated heterogeneous system based on visual-lidar fusion[J]. Aerospace, 2023, 10(11): 924. doi: 10.3390/aerospace10110924.
    [22]
    LI Yangyan, BU Rui, SUN Mingchao, et al. PointCNN: Convolution on X-transformed points[C]. The 32nd International Conference on Neural Information Processing Systems, Montréal Canada, 2018: 828–838.
    [23]
    GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: The KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231–1237. doi: 10.1177/0278364913491297.
    [24]
    CAESAR H, BANKITI V, LANG A H, et al. nuScenes: A multimodal dataset for autonomous driving[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 11621–11631. DOI: 10.1109/CVPR42600.2020.01164.
    [25]
    MADDERN W, PASCOE G, LINEGAR C, et al. 1 year, 1000 km: The oxford robotcar dataset[J]. The International Journal of Robotics Research, 2017, 36(1): 3–15. doi: 10.1177/0278364916679498.
    [26]
    CURLESS B and LEVOY M. A volumetric method for building complex models from range images[C]. The 23rd Annual Conference on Computer Graphics and Interactive Techniques, New Orleans, USA, 1996: 303–312. doi: 10.1145/237170.237269.
    [27]
    MIAN A S, BENNAMOUN M, and OWENS R A. A novel representation and feature matching algorithm for automatic pairwise registration of range images[J]. International Journal of Computer Vision, 2006, 66(1): 19–40. doi: 10.1007/s11263-005-3221-0.
    [28]
    SUN Pei, KRETZSCHMAR H, DOTIWALLA X, et al. Scalability in perception for autonomous driving: Waymo open dataset[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 2446–2454. doi: 10.1109/CVPR42600.2020.00252.
    [29]
    GEYER J, KASSAHUN Y, MAHMUDI M, et al. A2D2: Audi autonomous driving dataset[J]. arXiv preprint arXiv, 2004.06320, 2020.
    [30]
    HUANG Xinyu, CHENG Xinjing, GENG Qichuan, et al. The apolloscape dataset for autonomous driving[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, USA, 2018: 954–960. doi: 10.1109/CVPRW.2018.00141.
    [31]
    CHANG Mingfang, LAMBERT J, SANGKLOY P, et al. Argoverse: 3D tracking and forecasting with rich maps[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 8748–8757. doi: 10.1109/CVPR.2019.00895.
    [32]
    STURM J, BURGARD W, and CREMERS D. Evaluating egomotion and structure-from-motion approaches using the TUM RGB-D benchmark[C]. The Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems, Vilamoura, Algarve, 2012: 6.
    [33]
    CHOI Y, KIM N, HWANG S, et al. KAIST multi-spectral day/night data set for autonomous and assisted driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 934–948. doi: 10.1109/TITS.2018.2791533.
    [34]
    CHOI S, ZHOU Qianyi, and KOLTUN V. Robust reconstruction of indoor scenes[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 5556–5565. doi: 10.1109/CVPR.2015.7299195.
    [35]
    ZENG A, SONG S, NIEßNER M, et al. 3DMatch: Learning local geometric descriptors from RGB-D reconstructions[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1802–1811. doi: 1 0.1109/CVPR.2017.29.
    [36]
    ABDELAZEEM M, ELAMIN A, AFIFI A, et al. Multi-sensor point cloud data fusion for precise 3D mapping[J]. The Egyptian Journal of Remote Sensing and Space Science, 2021, 24(3): 835–844. doi: 10.1016/j.ejrs.2021.06.002.
    [37]
    CÓRDOVA-ESPARZA D M, TERVEN J R, JIMÉNEZ-HERNÁNDEZ H, et al. A multiple camera calibration and point cloud fusion tool for Kinect V2[J]. Science of Computer Programming, 2017, 143: 1–8. doi: 10.1016/j.scico.2016.11.004.
    [38]
    PANAGIOTIDIS D, ABDOLLAHNEJAD A, and SLAVÍK M. 3D point cloud fusion from UAV and TLS to assess temperate managed forest structures[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102917. doi: 10.1016/j.jag.2022.102917.
    [39]
    YANG Weijun, LIU Yang, HE Huagui, et al. Airborne LiDAR and photogrammetric point cloud fusion for extraction of urban tree metrics according to street network segmentation[J]. IEEE Access, 2021, 9: 97834–97842. doi: 10.1109/ACCESS.2021.3094307.
    [40]
    PARVAZ S, TEFERLE F, and NURUNNABI A. Airborne cross-source point clouds fusion by slice-to-slice adjustment[C]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Athens, Greece, 2024: 161–168. doi: 10.5194/isprs-annals-X-4-W4-2024-161-2024.
    [41]
    CHENG Hongtai and HAN Jiayi. Toward precise dense 3D reconstruction of indoor hallway: A confidence-based panoramic LiDAR point cloud fusion approach[J]. Industrial Robot, 2025, 52(1): 116–125. doi: 10.1108/IR-03-2024-0132.
    [42]
    LI Shiming, GE Xuming, HU Han, et al. Laplacian fusion approach of multi-source point clouds for detail enhancement[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 171: 385–396. doi: 10.1016/j.isprsjprs.2020.11.021.
    [43]
    BAI Zhengwei, WU Guoyuan, BARTH M J, et al. PillarGrid: Deep learning-based cooperative perception for 3D object detection from onboard-roadside LiDAR[C]. 2022 IEEE 25th International Conference on Intelligent Transportation Systems, Macau, China, 2022: 1743–1749. doi: 10.1109/ITSC55140.2022.9921947.
    [44]
    ZHANG Pengcheng, HE Huagui, WANG Yun, et al. 3D urban buildings extraction based on airborne LiDAR and photogrammetric point cloud fusion according to U-Net deep learning model segmentation[J]. IEEE Access, 2022, 10: 20889–20897. doi: 10.1109/ACCESS.2022.3152744.
    [45]
    GUO Lijie, WU Yanjie, DENG Lei, et al. A feature-level point cloud fusion method for timber volume of forest stands estimation[J]. Remote Sensing, 2023, 15(12): 2995. doi: 10.3390/rs15122995.
    [46]
    QIAO Donghao and ZULKERNINE F. Adaptive feature fusion for cooperative perception using LiDAR point clouds[C]. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2023: 1186–1195. doi: 10.1109/WACV56688.2023.00124.
    [47]
    GRIFONI E, VANNINI E, LUNGHI I, et al. 3D multi-modal point clouds data fusion for metrological analysis and restoration assessment of a panel painting[J]. Journal of Cultural Heritage, 2024, 66: 356–366. doi: 10.1016/j.culher.2023.12.007.
    [48]
    CHEN Qi, TANG Sihai, YANG Qing, et al. Cooper: Cooperative perception for connected autonomous vehicles based on 3D point clouds[C]. 2019 IEEE 39th International Conference on Distributed Computing Systems, Dallas, USA, 2019: 514–524. doi: 10.1109/ICDCS.2019.00058.
    [49]
    HURL B, COHEN R, CZARNECKI K, et al. TruPercept: Trust modelling for autonomous vehicle cooperative perception from synthetic data[C]. 2020 IEEE Intelligent Vehicles Symposium, Las Vegas, USA, 2020: 341–347. doi: 10.1109/IV47402.2020.9304695.
    [50]
    LI Jie, ZHUANG Yiqi, PENG Qi, et al. Pose estimation of non-cooperative space targets based on cross-source point cloud fusion[J]. Remote Sensing, 2021, 13(21): 4239. doi: 10.3390/rs13214239.
    [51]
    TANG Zhiri, HU Ruihan, CHEN Yanhua, et al. Multi-expert learning for fusion of pedestrian detection bounding box[J]. Knowledge-Based Systems, 2022, 241: 108254. doi: 10.1016/j.knosys.2022.108254.
    [52]
    YU Haibao, LUO Yizhen, SHU Mao, et al. DAIR-V2X: A large-scale dataset for vehicle-infrastructure cooperative 3D object detection[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 21361–21370. doi: 10.1109/CVPR52688.2022.02067.
    [53]
    RIZALDY A, AFIFI A J, GHAMISI P, et al. Dimensional dilemma: Navigating the fusion of hyperspectral and LiDAR point cloud data for optimal precision-2D vs. 3D[C]. 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024: 7729–7733. doi: 10.1109/IGARSS53475.2024.10641140.
    [54]
    LUO Wenjie, YANG Bin, and URTASUN R. Fast and furious: Real time end-to-end 3D detection, tracking and motion forecasting with a single convolutional net[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3569–3577. doi: 10.1109/CVPR.2018.00376.
    [55]
    LI Jing, LI Rui, WANG Junzheng, et al. Obstacle information detection method based on multiframe three-dimensional lidar point cloud fusion[J]. Optical Engineering, 2019, 58(11): 116102. doi: 10.1117/1.OE.58.11.116102.
    [56]
    HUANG Rui, ZHANG Wanyue, KUNDU A, et al. An LSTM approach to temporal 3D object detection in LiDAR point clouds[C]. The 16th European Conference on Computer Vision, Glasgow, UK, 2020: 266–282. doi: 10.1007/978-3-030-58523-5_16.
    [57]
    YIN Junbo, SHEN Jianbing, GUAN Chenye, et al. LiDAR-based online 3D video object detection with graph-based message passing and spatiotemporal transformer attention[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 11495–11504. doi: 10.1109/CVPR42600.2020.01151.
    [58]
    YANG Zetong, ZHOU Yin, CHEN Zhifeng, et al. 3D-MAN: 3D multi-frame attention network for object detection[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 1863–1872. doi: 10.1109/CVPR46437.2021.00190.
    [59]
    QI C R, ZHOU Yin, NAJIBI M, et al. Offboard 3D object detection from point cloud sequences[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 6134–6144. doi: 10.1109/CVPR46437.2021.00607.
    [60]
    SUN Pei, WANG Weiyue, CHAI Yuning, et al. RSN: Range sparse net for efficient, accurate LiDAR 3D object detection[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 5725–5734. doi: 10.1109/CVPR46437.2021.00567.
    [61]
    YIN Tianwei, ZHOU Xingyi, and KRÄHENBÜHL P. Center-based 3D object detection and tracking[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 11784–11793. doi: 10.1109/CVPR46437.2021.01161.
    [62]
    LUO Chenxu, YANG Xiaodong, and YUILLE A. Exploring simple 3D multi-object tracking for autonomous driving[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 10488–10497. doi: 10.1109/ICCV48922.2021.01032.
    [63]
    毛冬海, 李守军, 王锋, 等. LiDAR测量点云融合影像的分块滤波方法[J]. 测绘通报, 2021(10): 67–72, 131. doi: 10.13474/j.cnki.11-2246.2021.307.

    MAO Donghai, LI Shoujun, WANG Feng, et al. Block filtering method for LiDAR point cloud fusion image[J]. Bulletin of Surveying and Mapping, 2021(10): 67–72, 131. doi: 10.13474/j.cnki.11-2246.2021.307.
    [64]
    CHEN Xuesong, SHI Shaoshuai, ZHU Benjin, et al. MPPNet: Multi-frame feature intertwining with proxy points for 3D temporal object detection[C]. The 17th European Conference on Computer Vision, Tel Aviv, Israel, 2022: 680–697. doi: 10.1007/978-3-031-20074-8_39.
    [65]
    HU Yihan, DING Zhuangzhuang, GE Runzhou, et al. AFDetV2: Rethinking the necessity of the second stage for object detection from point clouds[C]. The 36th AAAI Conference on Artificial Intelligence, 2022: 969–979. doi: 10.1609/aaai.v36i1.19980.
    [66]
    HE Chenhang, LI Ruihuang, ZHANG Yabin, et al. MSF: Motion-guided sequential fusion for efficient 3D object detection from point cloud sequences[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 5196–5205. doi: 10.1109/CVPR52729.2023.00503.
    [67]
    SHI Lingfeng, LV Yunfeng, YIN Wei, et al. Autonomous multiframe point cloud fusion method for mmWave radar[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 4505708. doi: 10.1109/TIM.2023.3302936.
    [68]
    YANG Yanding, JIANG Kun, YANG Diange, et al. Temporal point cloud fusion with scene flow for robust 3D object tracking[J]. IEEE Signal Processing Letters, 2022, 29: 1579–1583. doi: 10.1109/LSP.2022.3185948.
    [69]
    CHEN Hui, FENG Yan, YANG Jian, et al. 3D reconstruction approach for outdoor scene based on multiple point cloud fusion[J]. Journal of the Indian Society of Remote Sensing, 2019, 47(10): 1761–1772. doi: 10.1007/s12524-019-01029-y.
    [70]
    CHENG Yuqi, LI Wenlong, JIANG Cheng, et al. MVGR: Mean-variance minimization global registration method for multiview point cloud in robot inspection[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 8504915. doi: 10.1109/TIM.2024.3413191.
    [71]
    LIU Junjie, LIU Junlong, YAN Shaotian, et al. MPC: Multi-view probabilistic clustering[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 9509–9518. doi: 10.1109/CVPR52688.2022.00929.
    [72]
    KLOEKER L, KOTULLA C, and ECKSTEIN L. Real-time point cloud fusion of multi-LiDAR infrastructure sensor setups with unknown spatial location and orientation[C]. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, Rhodes, Greece, 2020: 1–8. doi: 10.1109/ITSC45102.2020.9294312.
    [73]
    YU Xumin, TANG Lulu, RAO Yongming, et al. Point-BERT: Pre-training 3D point cloud transformers with masked point modeling[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 19313–19322. doi: 10.1109/CVPR52688.2022.01871.
    [74]
    LI Yao, ZHOU Yong, ZHAO Jiaqi, et al. FA-MSVNet: Multi-scale and multi-view feature aggregation methods for stereo 3D reconstruction[J]. Multimedia Tools and Applications, 2024: 1–23. doi: 10.1007/s11042-024-20431-4.
    [75]
    CHEN Zhimin, LI Yingwei, JING Longlong, et al. Point cloud self-supervised learning via 3D to multi-view masked autoencoder[J]. arXiv preprint arXiv, 2311.10887, 2023.
    [76]
    LING Xiao and QIN Rongjun. A graph-matching approach for cross-view registration of over-view and street-view based point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 185: 2–15. doi: 10.1016/j.isprsjprs.2021.12.013.
    [77]
    深圳市博大建设集团有限公司. 一种多视角三维激光点云拼接方法及系统[P]. 中国, 202411659468.1, 2024.

    Shenzhen Boda Construction Group Co., Ltd. Multi-view three-dimensional laser point cloud splicing method and system[P]. CN, 202411659468.1, 2024.
    [78]
    北京鹏太光耀科技有限公司. 一种基于多目结构光的全向点云融合方法[P]. 中国, 202411346535.4, 2025.

    Beijing Pengtai Guangyao Technology Co., Ltd. Omnidirectional point cloud fusion method based on multi-view structured light[P]. CN, 202411346535.4, 2025.
    [79]
    KHAN K N, KHALID A, TURKAR Y, et al. VRF: Vehicle road-side point cloud fusion[C]. The 22nd Annual International Conference on Mobile Systems, Applications and Services, Tokyo, Japan, 2024: 547–560. doi: 10.1145/3643832.3661874.
    [80]
    BREYER M, OTT L, SIEGWART R, et al. Closed-loop next-best-view planning for target-driven grasping[C]. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyoto, Japan, 2022: 1411–1416. doi: 10.1109/IROS47612.2022.9981472.
    [81]
    JIANG Changjian, GAO Ruilan, SHAO Kele, et al. LI-GS: Gaussian splatting with LiDAR incorporated for accurate large-scale reconstruction[J]. IEEE Robotics and Automation Letters, 2025, 10(2): 1864–1871. doi: 10.1109/LRA.2024.3522846.
    [82]
    YANG Shuo, LU Huimin, and LI Jianru. Multifeature fusion-based object detection for intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(1): 1126–1133. doi: 10.1109/TITS.2022.3155488.
    [83]
    MA Nan, XIAO Chuansheng, WANG Mohan, et al. A review of point cloud and image cross-modal fusion for self-driving[C]. 2022 18th International Conference on Computational Intelligence and Security, Chengdu, China, 2022: 456–460. doi: 10.1109/CIS58238.2022.00103.
    [84]
    LIU Zhijian, TANG Haotian, AMINI A, et al. BEVFusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation[C]. 2023 IEEE International Conference on Robotics and Automation, London, UK, 2023: 2774–2781. doi: 10.1109/ICRA48891.2023.10160968.
    [85]
    KUMAR G A, LEE J H, HWANG J, et al. LiDAR and camera fusion approach for object distance estimation in self-driving vehicles[J]. Symmetry, 2020, 12(2): 324. doi: 10.3390/sym12020324.
    [86]
    HUANG Keli, SHI Botian, LI Xiang, et al. Multi-modal sensor fusion for auto driving perception: A survey[J]. arXiv preprint arXiv, 2202.02703, 2022.
    [87]
    CAI Yiyi, OU Yang, and QIN Tuanfa. Improving SLAM techniques with integrated multi-sensor fusion for 3D reconstruction[J]. Sensors, 2024, 24(7): 2033. doi: 10.3390/s24072033.
    [88]
    RODRIGUEZ-GARCIA B, RAMÍREZ-SANZ J M, MIGUEL-ALONSO I, et al. Enhancing learning of 3D model unwrapping through virtual reality serious game: Design and usability validation[J]. Electronics, 2024, 13(10): 1972. doi: 10.3390/electronics13101972.
    [89]
    KAVALIAUSKAS P, FERNANDEZ J B, MCGUINNESS K, et al. Automation of construction progress monitoring by integrating 3D point cloud data with an IFC-based BIM model[J]. Buildings, 2022, 12(10): 1754. doi: 10.3390/buildings12101754.
    [90]
    XU Yusheng and STILLA U. Toward building and civil infrastructure reconstruction from point clouds: A review on data and key techniques[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2857–2885. doi: 10.1109/JSTARS.2021.3060568.
    [91]
    JHONG S Y, CHEN Y Y, HSIA C H, et al. Density-aware and semantic-guided fusion for 3-D object detection using LiDAR-camera sensors[J]. IEEE Sensors Journal, 2023, 23(18): 22051–22063. doi: 10.1109/JSEN.2023.3302314.
    [92]
    LUO Zhipeng, ZHOU Changqing, PAN Liang, et al. Exploring point-BEV fusion for 3D point cloud object tracking with transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(9): 5921–5935. doi: 10.1109/TPAMI.2024.3373693.
    [93]
    LIU Jianan, ZHAO Qiuchi, XIONG Weiyi, et al. SMURF: Spatial multi-representation fusion for 3D object detection with 4D imaging radar[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 799–812. doi: 10.1109/TIV.2023.3322729.
    [94]
    HAO Ruidong, WEI Zhongwei, HE Xu, et al. Robust point cloud registration network for complex conditions[J]. Sensors, 2023, 23(24): 9837. doi: 10.3390/s23249837.
    [95]
    BAYRAK O C, MA Zhenyu, MARIAROSARIA FARELLA E, et al. Operationalize large-scale point cloud classification: Potentials and challenges[C]. EGU General Assembly Conference Abstracts 2024, Vienna, Austria, 2024: 10034. doi: 10.5194/egusphere-egu24-10034.
    [96]
    SHAN Shuo, LI Chenxi, WANG Yiye, et al. A deep learning model for multi-modal spatio-temporal irradiance forecast[J]. Expert Systems with Applications, 2024, 244: 122925. doi: 10.1016/j.eswa.2023.122925.
    [97]
    QIAN Jiaming, FENG Shijie, TAO Tianyang, et al. Real-time 3D point cloud registration[C]. SPIE 11205, Seventh International Conference on Optical and Photonic Engineering, Phuket, Thailand, 2019: 495–500. doi: 10.1117/12.2547865.
    [98]
    SHI Fengyuan, ZHENG Xunjiang, JIANG Lihui, et al. Fast point cloud registration algorithm using parallel computing strategy[J]. Journal of Physics: Conference Series, 2022, 2235(1): 012104. doi: 10.1088/1742-6596/2235/1/012104.
    [99]
    LUO Yukui. Securing FPGA as a shared cloud-computing resource: Threats and mitigations[D]. [Ph.D. dissertation], Northeastern University, 2023.
    [100]
    WANG Laichao, LU Weiding, TIAN Yuan, et al. 6D object pose estimation with attention aware bi-gated fusion[C]. The 30th International Conference on Neural Information Processing, Changsha, China, 2024: 573–585. doi: 10.1007/978-981-99-8082-6_44.
    [101]
    HONG Jiaxin, ZHANG Hongbo, LIU Jinghua, et al. A transformer-based multi-modal fusion network for 6D pose estimation[J]. Information Fusion, 2024, 105: 102227. doi: 10.1016/j.inffus.2024.102227.
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