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平行点云: 虚实互动的点云生成与三维模型进化方法

田永林 沈宇 李强 王飞跃

田永林, 沈宇, 李强, 王飞跃. 平行点云: 虚实互动的点云生成与三维模型进化方法. 自动化学报, 2020, 46(12): 2572−2582 doi: 10.16383/j.aas.c200800
引用本文: 田永林, 沈宇, 李强, 王飞跃. 平行点云: 虚实互动的点云生成与三维模型进化方法. 自动化学报, 2020, 46(12): 2572−2582 doi: 10.16383/j.aas.c200800
Tian Yong-Lin, Shen Yu, Li Qiang, Wang Fei-Yue. Parallel point clouds: point clouds generation and 3D model evolution via virtual-real interaction. Acta Automatica Sinica, 2020, 46(12): 2572−2582 doi: 10.16383/j.aas.c200800
Citation: Tian Yong-Lin, Shen Yu, Li Qiang, Wang Fei-Yue. Parallel point clouds: point clouds generation and 3D model evolution via virtual-real interaction. Acta Automatica Sinica, 2020, 46(12): 2572−2582 doi: 10.16383/j.aas.c200800

平行点云: 虚实互动的点云生成与三维模型进化方法

doi: 10.16383/j.aas.c200800
基金项目: 国家自然科学基金重点项目(61533019), 英特尔智能网联汽车大学合作研究中心项目(ICRI – IACV), 国家自然科学基金项目联合基金(U1811463), 广州市智能网联汽车重大科技专项(202007050002)资助
详细信息
    作者简介:

    田永林:中国科学技术大学与中国科学院自动化研究所联合培养博士研究生. 2017年获得中国科学技术大学自动化系学士学位. 主要研究方向为计算机视觉, 智能交通. E-mail: tyldyx@mail.ustc.edu.cn

    沈宇:中国科学院大学人工智能学院博士研究生. 主要研究方向为视频预测, 平行强化学习, 机器人自主导航. E-mail: shenyu2015@ia.ac.cn

    李强:中国科学院大学人工智能学院硕士研究生. 主要研究方向为图像识别, 平行无人车系统. E-mail: liqiang2018@ia.ac.cn

    王飞跃:中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员. 主要研究方向为智能系统和复杂系统的建模、分析与控制. 本文通信作者. E-mail: feiyue.wang@ia.ac.cn

Parallel Point Clouds: Point Clouds Generation and 3D Model Evolution via Virtual-real Interaction

Funds: Supported by the State Key Program of National Natural Science Foundation of China (61533019), the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI–IACV), the Joint Funds of the National Natural Science Foundation of China (U1811463), and the Key Research and Development Program of Guangzhou (202007050002)
  • 摘要: 三维信息的提取在自动驾驶等智能交通场景中正发挥着越来越重要的作用, 为了解决以激光雷达为主的深度传感器在数据采集方面面临的成本高、样本覆盖不全面等问题, 本文提出了平行点云的框架. 利用人工定义场景获取虚拟点云数据, 通过计算实验训练三维模型, 借助平行执行对模型性能进行测试, 并将结果反馈至数据生成和模型训练过程. 通过不断地迭代, 使三维模型得到充分评估并不断进化. 在平行点云的框架下, 我们以三维目标检测为例, 通过闭环迭代, 构建了虚实结合的点云数据集, 在无需人工标注的情况下, 可达到标注数据训练模型精度的72%.
  • 图  1  平行点云框架

    Fig.  1  The framework of parallel point clouds

    图  2  激光雷达扫描示意图

    Fig.  2  The illustration of LiDAR scanning

    图  3  CARLA仿真环境及虚拟雷达点云

    Fig.  3  CARLA simulation environment and virtual LiDAR point clouds

    图  4  KITTI伪雷达点云生成方法

    Fig.  4  Pesudo LiDAR point clouds generation with KITTI dataset

    图  5  虚实结合的点云生成方法

    Fig.  5  LiDAR point clouds generation via virtual-real interaction

    图  6  KITTI (左)与ShapeKITTI (右)点云数据可视化对比

    Fig.  6  The visualization of point clouds from KITTI (left) and ShapeKITTI (right)

    图  7  点云数据生成对三维检测模型性能的影响

    Fig.  7  The influence of point clouds generation on the 3D detector performance

    图  8  不同距离下的虚拟点云生成效果

    Fig.  8  The visualization of virtual point clouds under difierent distance

    图  9  不同方位角的虚拟点云生成效果

    Fig.  9  The visualization of virtual point clouds under difierent azimuths

    图  10  KITTI数据集中的长尾分布

    Fig.  10  The long-tailed distribution in KITTI dataset

    表  1  激光雷达属性描述

    Table  1  The description of LiDAR attributes

    属性描述默认值
    Channels激光器数量32
    Range测量/射线广播的
    最大距离(米)
    10.0
    Points per second每秒所有激光
    器产生的点
    56000
    Rotation frequency激光雷达旋转频率10.0
    Upper field of view最高激光的角度(度)10.0
    Lower field of view最低激光的角度(度)−30
    Atmosphere attenuation rate测量每米LiDAR强
    度损失的系数
    0.004
    Dropoff general rate随机掉落的总点数比例0.45
    Dropoff intensity limit对于基于强度的下降,阈值
    强度值不超过任何点
    0.8
    下载: 导出CSV

    表  2  基于ShapeKITTI的三维目标检测模型(PointPillars)平均精度 (%)

    Table  2  The average precision of 3D object detector (PointPillars) based on ShapeKITTI dataset (%)

    评测项目简单模式中等模式困难模式
    3D AP (Real)85.2175.8669.21
    3D AP (Ours)71.4454.3952.30
    BEV AP (Real)89.7987.4484.77
    BEV AP (Ours)82.8170.4465.68
    AOS AP (Real)90.6389.3488.36
    AOS AP (Ours)84.0269.6568.08
    下载: 导出CSV

    表  3  基于ShapeKITTI的三维目标检测模型(SECOND)平均精度 (%)

    Table  3  The average precision of 3D object detector (SECOND) based on ShapeKITTI dataset (%)

    评测项目简单模式中等模式困难模式
    3D AP (Real)87.4376.4869.10
    3D AP (Ours)59.6741.1837.67
    BEV AP (Real)89.5287.2883.89
    BEV AP (Ours)73.5553.3752.34
    AOS AP (Real)90.4989.4588.41
    AOS AP (Ours)75.0154.7254.13
    下载: 导出CSV

    表  4  基于闭环反馈的三维检测模型性能进化过程 (%)

    Table  4  The evolutionary process of 3D detection model based on the closed-loop feedback (%)

    序号AP (简单)AP (中等)AP (困难)反馈建议AP提升
    G010.6410.3210.53仅对少数目标能有效检测增加CAD模型种类
    G122.1416.4613.98目标尺寸估计欠佳改进CAD模型尺寸6.14 (G0 to G1)
    G232.8522.4019.52对稀疏目标检测效果欠佳降低前景目标密度5.94 (G1 to G2)
    G329.4326.8523.51目标高度估计效果欠佳调整前景目标高度4.45 (G2 to G3)
    G438.9929.7427.10对稀疏目标检测效果仍然欠佳进一步降低前景目标密度2.89 (G3 to G4)
    G539.2231.0430.302.30 (G4 to G5)
    下载: 导出CSV
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  • 收稿日期:  2020-09-26
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