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基于PointPillars的多模态融合三维目标检测方法

王睿智，张虹

（太原师范学院，山西 晋中 030619）

栏目：人工智能 · 第 2025 年第 5 期 · 页码：56-61 · 2096-4706（2025）05-0056-06 · 中图分类号：TP183；TP391 · 文献标识码：A · DOI：10.19850/j.cnki.2096-4706.2025.05.010

摘要

点云数据的稀疏性和无序性会导致相关检测算法在远小目标检测中容易出现漏检和误检的问题。因此，文章基于PointPillars算法提出了一种多模态融合的三维目标检测算法。该算法设计了一种多模态特征融合柱体化编码模块，能够融合点云特征和图像特征，从而增强特征的语义信息，提高远小目标的检测精度。在KITTI数据集上的实验结果显示，与基线模型相比，汽车、行人和骑行者类别的三维平均检测精度分别提高了3.28%、2.88%和1.62%。结果表明，所提出的基于PointPillars的多模态融合三维目标检测方法能够有效减少远小目标的误检和漏检。

关键词

多模态融合；三维目标检测；柱体化编码；PointPillars

参考文献

  1. [1] QIAN R，LAI X，LI X R. 3D Object Detection for Autonomous Driving: A Survey [J/OL].Pattern Recognition，2022，130：108796[2024-08-26].https://doi.org/10.1016/j.patcog.2022.108796.
  2. [2] MA X Z，OUYANG W L，SIMONELLI A，et al. 3D Object Detection From Images for Autonomous Driving：A Survey [J].IEEE Transactions On Pattern Analysis And Machine Intelligence，2024，46（5）：3537-3556.
  3. [3] WANG Y J，MAO Q Y，ZHU H Q，et al. Multi-Modal 3D Object Detection in Autonomous Driving: A Survey [J].International Journal of Computer Vision，2023，131（8）：2122-2152.
  4. [4] LANG A H，VORA S，CAESAR H，et al. PointPillars: Fast Encoders for Object Detection From Point Clouds [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Long Beach：IEEE，2019：12689-12697.
  5. [5] HUANG K L，SHI B T，LI X，et al. Multi-modal Sensor Fusion for Auto Driving Perception: A Survey [J/OL].arXiv:2202.02703 [cs.CV].[2024-08-03].https://doi.org/10.48550/arXiv.2202.02703.
  6. [6] VORA S，LANG A H，HELOU B，et al. PointPainting: Sequential Fusion for 3D Object Detection [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle：IEEE，2020：4604-4612.
  7. [7] CHEN X Z，MA H M，WAN J，et al. Multi-View 3D Object Detection Network for Autonomous Driving [J/OL].arXiv:1611.07759 [cs.CV].[2024-08-15].https://doi.org/10.48550/arXiv.1611.07759.
  8. [8] KU J，MOZIFIAN M，LEE J，et al. Joint 3D Proposal Generation and Object Detection from View Aggregation [C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Madrid：IEEE，2018：1-8.
  9. [9] XU D F，ANGUELOV D，LAIN A. PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City：IEEE，2018：244-253.
  10. [10] HUANG T T，LIU Z，CHEN X W，et al. EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection [C]//Computer Vision-ECCV 2020.Glasgow：Springer，2020：35-52.
  11. [11] PAIGWAR A，SIERRA-GONZALEZ D，ERKENT O，et al. Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR [C]//.2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).Montreal：IEEE，2021：2926-2933.
  12. [12] WANG K，ZHOU T Q，ZHANG Z C，et al. PVF-DectNet: Multi-modal 3D Detection Network Based on Perspective-Voxel Fusion [J/OL].Engineering Applications of Artificial Intelligence，2023，120：105951[2024-07-26].https://doi.org/10.1016/j.engappai.2023.105951.
  13. [13] CHEN Z H，LI Z Y，ZHANG S Q，et al. AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection [J/OL].arXiv:2202.02703 [cs.CV].[2024-08-11].https://doi.org/10.48550/arXiv.2202.02703.
  14. [14] ZHOU Y，TUZEL O. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City：IEEE，2018：4490-4499.
  15. [15] YAN Y，MAO Y X，LI B. SECOND:Sparsely Embedded Convolutional Detection [J/OL]. Sensors，2018，18（10）：3337[2024-07-23].https://doi.org/10.3390/s18103337.
  16. [16] LIU Z，ZHAO X，HUANG T T，et al. TANet: Robust 3D Object Detection from Point Clouds with Triple Attention [C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York：AAAI，2020，34（7）：11677-11684.
  17. [17] PAN X R，XIA Z F，SONG S J，et al. 3D Object Detection with Pointformer [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Nashville：IEEE，2021：7459-7468.
  18. [18] SHI W J，RAJKUMAR R. Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle：IEEE，2020：1708-1716.
  19. [19] HE C H，ZENG H，HUANG J Q，et al. Structure Aware Single-Stage 3D Object Detection From Point Cloud [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle：IEEE，2020：11870–11879.

基金项目

太原师范学院研究生教育创新项目（SYYJSYC-2395）

作者简介

王睿智（1999—），男，汉族，山西运城人，硕士研究生在读，研究方向：三维目标检测、图像处理；通信作者：张虹（1977—），女，汉族，山西太原人，副教授，博士，研究方向：人工智能、区块链与智能数据。