• Laser & Optoelectronics Progress
  • Vol. 62, Issue 6, 0615012 (2025)
Gan Zhang*, Yuhui Peng, Baozhe Sun, Shenyang Lin, and Jiaming Zhang
Author Affiliations
  • School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, Fujian , China
  • show less
    DOI: 10.3788/LOP241730 Cite this Article Set citation alerts
    Gan Zhang, Yuhui Peng, Baozhe Sun, Shenyang Lin, Jiaming Zhang. Improved PointPillars Algorithm Based on Point Cloud Feature Enhancement[J]. Laser & Optoelectronics Progress, 2025, 62(6): 0615012 Copy Citation Text show less
    References

    [1] Qin J, Wang W B, Zou Q Jet al. A survey of 3D object detection methods based on lidar point clouds[J]. Computer Science, 50, 259-265(2023).

    [2] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https:?∥arxiv.org/abs/1706.02413v1

    [3] Shi S S, Wang X G, Li H S. PointRCNN: 3D object proposal generation and detection from point cloud[C], 770-779(2019).

    [4] Yang Z T, Sun Y N, Liu S et al. STD: sparse-to-dense 3D object detector for point cloud[C], 1951-1960(2019).

    [5] Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C], 4490-4499(2018).

    [6] Deng J J, Shi S S, Li P W et al. Voxel R-CNN: toward high performance voxel-based 3D object detection[EB/OL]. https:?∥arxiv.org/abs/2012.15712

    [7] Lang A H, Vora S, Caesar H et al. PointPillars: fast encoders for object detection from point clouds[C], 12689-12697(2019).

    [8] Tian F, Liu C, Liu F et al. Laser radar 3D target detection based on improved PointPillars[J]. Laser & Optoelectronics Progress, 61, 0812007(2024).

    [9] Liu Z, Zhao X, Huang T T et al. TANet: robust 3D object detection from point clouds with triple attention[EB/OL]. https:?∥arxiv.org/abs/1912.05163

    [10] Li J Y, Luo C X, Yang X D. PillarNeXt: rethinking network designs for 3D object detection in LiDAR point clouds[C], 17567-17576(2023).

    [11] Wang L Z, Huang M H, Liu R Y et al. Improved two-stage 3D object detection algorithm for roadside scenes with enhanced PointPillars and transformer[J]. Laser & Optoelectronics Progress, 61, 2037010(2024).

    [12] Zhan W Q, Ni R, Yang B. An attention-based PointPillars+3D object detection[J]. Journal of Jiangsu University (Natural Science Edition), 41, 268-273(2020).

    [13] Yang Q X, Kong D M, Chen J et al. PointPillars improvement based on density clustering and dual attention mechanisms[J]. Laser & Optoelectronics Progress, 61, 2412003(2024).

    [14] Ouyang D L, He S, Zhang G Z et al. Efficient multi-scale attention module with cross-spatial learning[C](2023).

    [15] Yan Y, Mao Y X, Li B. SECOND: sparsely embedded convolutional detection[J]. Sensors, 18, 3337(2018).

    [16] Chen S W. Research on 3D point cloud object detection technology for autonomous vehicles based on deep learning[D], 36-37(2023).

    [17] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C], 3354-3361(2012).

    [18] Chen D J, Yu W J, Gao Y B. Lidar 3D target detection based on improved PointPillars[J]. Laser & Optoelectronics Progress, 60, 1028012(2023).

    [19] Huang Z T, Yu Y K, Xu J W et al. PF-net: point fractal network for 3D point cloud completion[C], 7659-7667(2020).

    [20] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

    [21] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

    [22] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

    [23] He K M, Zhang X Y, Ren S Qet al. Deep residual learning for image recognition[C], 770-778(2016).

    [24] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

    [25] Qi C R, Liu W, Wu C X et al. Frustum PointNets for 3D object detection from RGB-D data[C], 918-927(2018).

    [26] Chen Y K, Liu J H, Zhang X Y et al. VoxelNeXt: fully sparse VoxelNet for 3D object detection and tracking[C], 21674-21683(2023).