Page 56 - 《软件学报》2026年第5期
P. 56
唐文能 等: 交通场景多模态双阶反馈的三维目标检测方法 1935
[51] Imran S, Liu XM, Morris D. Depth completion with twin surface extrapolation at occlusion boundaries. In: Proc. of the 2021 IEEE/CVF
Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 2583–2592. [doi: 10.1109/CVPR46437.2021.00261]
[52] OpenPCDet Development Team. OpenPCDet: An open-source toolbox for 3D object detection from point clouds. 2020. https://github.
com/open-mmlab/OpenPCDet
[53] Liang M, Yang B, Wang SL, Urtasun R. Deep continuous fusion for multi-sensor 3D object detection. In: Proc. of the 15th European
Conf. on Computer Vision (ECCV). Munich: Springer, 2018. 663–678. [doi: 10.1007/978-3-030-01270-0_39]
[54] Liang M, Yang B, Chen Y, Hu R, Urtasun R. Multi-task multi-sensor fusion for 3D object detection. In: Proc. of the 2019 IEEE/CVF
Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 7337–7345. [doi: 10.1109/CVPR.2019.00752]
[55] Wang ZJ, Zhao Z, Jin Z, Che ZP, Tang J, Shen CM, Peng YX. Multi-stage fusion for multi-class 3D lidar detection. In: Proc. of the 2021
IEEE/CVF Int’l Conf. on Computer Vision Workshops. Montreal: IEEE, 2021. 3113–3121. [doi: 10.1109/ICCVW54120.2021.00347]
[56] Chen YK, Li YW, Zhang XY, Sun J, Jia JY. Focal sparse convolutional networks for 3D object detection. In: Proc. of the 2022
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 5418–5427. [doi: 10.1109/CVPR52688.2022.
00535]
[57] Yang HH, Liu ZL, Wu XP, Wang WX, Qian W, He XF, Cai D. Graph R-CNN: Towards accurate 3D object detection with semantic-
decorated local graph. In: Proc. of the 17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 662–679. [doi: 10.1007/978-3-
031-20074-8_38]
[58] Lin ZW, Shen YQ, Zhou SP, Chen ST, Zheng NN. MLF-DET: Multi-level fusion for cross-modal 3D object detection. In: Proc. of the
32nd Int’l Conf. on Artificial Neural Networks. Heraklion: Springer, 2023. 136–149. [doi: 10.1007/978-3-031-44195-0_12]
[59] Wang ML, Zhao L, Yue YF. PA3DNet: 3-D vehicle detection with pseudo shape segmentation and adaptive camera-LiDAR fusion. IEEE
Trans. on Industrial Informatics, 2023, 19(11): 10693–10703. [doi: 10.1109/TII.2023.3241585]
[60] Tian YL, Zhang XJ, Wang X, Xu JT, Wang JG, Ai R, Gu WH, Ding WP. ACF-Net: Asymmetric cascade fusion for 3D detection with
LiDAR point clouds and images. IEEE Trans. on Intelligent Vehicles, 2024, 9(2): 3360–3371. [doi: 10.1109/TIV.2023.3341223]
[61] Zhang GX, Xie J, Liu L, Wang ZP, Yang KH, Song ZY. URFormer: Unified representation LiDAR-camera 3D object detection with
Transformer. In: Proc. of the 6th Chinese Conf. on Pattern Recognition and Computer Vision (PRCV). Xiamen: Springer, 2024. 401–413.
[doi: 10.1007/978-981-99-8435-0_32]
附中文参考文献
[1] 相迎宵, 李轶珂, 刘吉强, 王潇瑾, 陈彤, 童恩栋, 牛温佳, 韩臻. 面向降频污染攻击的智能交通拥堵态势量化分析. 软件学报, 2023,
34(2): 833–848. http://www.jos.org.cn/1000-9825/6416.htm [doi: 10.13328/j.cnki.jos.006416]
[2] 陈存铜, 赵君峤, 叶晨, 邓蓉, 管林挺, 李德毅. 基于共享内存的智能无人车进程间消息异步传输机制. 软件学报, 2017, 28(5):
1315–1325. http://www.jos.org.cn/1000-9825/5144.htm [doi: 10.13328/j.cnki.jos.005144]
[3] 李庚松, 刘艺, 郑奇斌, 杨国利, 刘坤, 王强, 刁兴春. 无人机多传感器数据融合研究综述. 软件学报, 2025, 36(4): 1881–1905. http://
www.jos.org.cn/1000-9825/7273.htm [doi: 10.13328/j.cnki.jos.007273]
[4] 孙海峰, 穆正阳, 戚琦, 王敬宇, 刘聪, 廖建新. 基于动态门控特征融合的轻量深度补全算法. 软件学报, 2023, 34(4): 1765–1778. http://
www.jos.org.cn/1000-9825/6399.htm [doi: 10.13328/j.cnki.jos.006399]
作者简介
唐文能, 硕士生, 主要研究领域为计算机视觉, 三维目标检测.
李垚辰, 博士, 副教授, 博士生导师, CCF 专业会员, 主要研究领域为计算机视觉, 智能交通, 具身智能.
高笙景, 硕士生, 主要研究领域为计算机视觉, 三维目标检测.
高聪, 硕士生, 主要研究领域为计算机视觉, 三维目标检测.
彭越涵, 硕士生, 主要研究领域为计算机视觉, 三维目标检测.
刘跃虎, 博士, 教授, 博士生导师, 主要研究领域为计算机视觉, 具身智能.

