Page 55 - 《软件学报》2026年第5期
P. 55

1934                                                       软件学报  2026  年第  37  卷第  5  期


                 [30]   Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-end object detection with Transformers. In: Proc. of the
                     16th European Conf. on Computer Vision. Glasgow: Springer, 2020. 213–229. [doi: 10.1007/978-3-030-58452-8_13]
                 [31]   Zhu BJ, Wang Z, Shi SS, Xu H, Hong LQ, Li HS. ConQueR: Query contrast voxel-DETR for 3D object detection. In: Proc. of the 2023
                     IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 9296–9305. [doi: 10.1109/CVPR52729.2023.
                     00897]
                 [32]   Zhou C, Zhang YN, Chen JX, Huang D. OcTr: Octree-based Transformer for 3D object detection. In: Proc. of the 2023 IEEE/CVF Conf.
                     on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 5166–5175. [doi: 10.1109/CVPR52729.2023.00500]
                 [33]   Huang TT, Liu Z, Chen XW, Bai X. EPNet: Enhancing point features with image semantics for 3D object detection. In: Proc. of the 16th
                     European Conf. on Computer Vision. Glasgow: Springer, 2020. 35–52. [doi: 10.1007/978-3-030-58555-6_3]
                 [34]   Liu Z, Huang TT, Li BL, Chen XW, Wang X, Bai X. EPNet++: Cascade bi-directional fusion for multi-modal 3D object detection. IEEE
                     Trans. on Pattern Analysis and Machine Intelligence, 2023, 45(7): 8324–8341. [doi: 10.1109/TPAMI.2022.3228806]
                 [35]   Vora S, Lang AH, Helou B, Beijbom O. PointPainting: Sequential fusion for 3D object detection. In: Proc. of the 2020 IEEE/CVF Conf.
                     on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 4603–4611. [doi: 10.1109/CVPR42600.2020.00466]
                 [36]   Wang  CW,  Ma  C,  Zhu  M,  Yang  XK.  PointAugmenting:  Cross-modal  augmentation  for  3D  object  detection.  In:  Proc.  of  the  2021
                     IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 11789–11798. [doi: 10.1109/CVPR46437.2021.
                     01162]
                 [37]   Yin  TW,  Zhou  XY,  Krähenbühl  P.  Multimodal  virtual  point  3D  detection.  In:  Proc.  of  the  35th  Int’l  Conf.  on  Neural  Information
                     Processing Systems. Curran Associates Inc., 2021. 1261.
                 [38]   Chen XZ, Ma HM, Wan J, Li B, Xia T. Multi-view 3D object detection network for autonomous driving. In: Proc. of the 2017 IEEE
                     Conf. on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 6526–6534. [doi: 10.1109/CVPR.2017.691]
                 [39]   Ku J, Mozifian M, Lee J, Harakeh A, Waslander SL. Joint 3D proposal generation and object detection from view aggregation. In: Proc.
                     of  the  2018  IEEE/RSJ  Int’l  Conf.  on  Intelligent  Robots  and  Systems  (IROS).  Madrid:  IEEE,  2018.  1–8.  [doi:  10.1109/IROS.2018.
                     8594049]
                 [40]   Yoo JH, Kim Y, Kim J, Choi JW. 3D-CVF: Generating joint camera and LiDAR features using cross-view spatial feature fusion for 3D
                     object detection. In: Proc. of the 16th European Conf. on Computer Vision. Glasgow: Springer, 2020. 720–736. [doi: 10.1007/978-3-030-
                     58583-9_43]
                 [41]   Pang S, Morris D, Radha H. CLOCs: Camera-LiDAR object candidates fusion for 3D object detection. In: Proc. of the 2020 IEEE/RSJ Int’l
                     Conf. on Intelligent Robots and Systems (IROS). Las Vegas: IEEE, 2020. 10386–10393. [doi: 10.1109/IROS45743.2020.9341791]
                 [42]   Pang S, Morris D, Radha H. Fast-CLOCs: Fast camera-LiDAR object candidates fusion for 3D object detection. In: Proc. of the 2022
                     IEEE/CVF Winter Conf. on Applications of Computer Vision. Waikoloa: IEEE, 2022. 3747–3756. [doi: 10.1109/WACV51458.2022.
                     00380]
                 [43]   Shen BQ, Dai SW, Chen YY, Xiong R, Wang Y, Jiao YM. GOOD: General optimization-based fusion for 3D object detection via LiDAR-
                     camera object candidates. arXiv:2303.09800, 2023.
                 [44]   Qi CR, Liu W, Wu CX, Su H, Guibas LJ. Frustum PointNets for 3D object detection from RGB-D data. In: Proc. of the 2018 IEEE/CVF
                     Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 918–927. [doi: 10.1109/CVPR.2018.00102]
                 [45]   Xu DF, Anguelov D, Jain A. PointFusion: Deep sensor fusion for 3D bounding box estimation. In: Proc. of the 2018 IEEE/CVF Conf. on
                     Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 244–253. [doi: 10.1109/CVPR.2018.00033]
                 [46]   Wang ZX, Jia K. Frustum ConvNet: Sliding frustums to aggregate local point-wise features for amodal 3D object detection. In: Proc. of
                     the 2019 IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS). Macao: IEEE, 2019. 1742–1749. [doi: 10.1109/IROS40897.
                     2019.8968513]
                 [47]   Zhu HQ, Deng JJ, Zhang Y, Ji JM, Mao QY, Li HQ, Zhang YY. VPFNet: Improving 3D object detection with virtual point based LiDAR
                     and stereo data fusion. IEEE Trans. on Multimedia, 2023, 25: 5291–5304. [doi: 10.1109/TMM.2022.3189778]
                 [48]   Wu XP, Peng L, Yang HH, Xie L, Huang CX, Deng CQ, Liu HF, Cai D. Sparse fuse dense: Towards high quality 3D detection with
                     depth  completion.  In:  Proc.  of  the  2022  IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern  Recognition.  New  Orleans:  IEEE,  2022.
                     5418–5427. [doi: 10.1109/CVPR52688.2022.00534]
                 [49]   Bai XY, Hu ZY, Zhu XG, Huang QQ, Chen YL, Fu HB, Tai CL. TransFusion: Robust LiDAR-camera fusion for 3D object detection with
                     Transformers.  In:  Proc.  of  the  2022  IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern  Recognition.  New  Orleans:  IEEE,  2022.
                     1080–1089. [doi: 10.1109/CVPR52688.2022.00116]
                 [50]   Li YW, Yu AW, Meng TJ, Caine B, Ngiam J, Peng DY, Shen JY, Lu YF, Zhou D, Le QV, Yuille A, Tan MX. DeepFusion: LiDAR-
                     camera  deep  fusion  for  multi-modal  3D  object  detection.  In:  Proc.  of  the  2022  IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern
                     Recognition. New Orleans: IEEE, 2022. 17161–17170. [doi: 10.1109/CVPR52688.2022.01667]
   50   51   52   53   54   55   56   57   58   59   60