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

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

田永林^{1, 2,},
沈宇^{2, 3,},
李强^{2, 3,},
王飞跃^{2, 4,}

1.
中国科学技术大学自动化系合肥 230027
2.
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北京 100190
3.
中国科学院大学人工智能学院北京 100049
4.
青岛智能产业技术研究院青岛 266000

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

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出版历程
- 收稿日期: 2020-09-26
- 网络出版日期: 2020-12-29
- 刊出日期: 2020-12-29

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

TIAN Yong-Lin^{1, 2
,},
SHEN Yu^{2, 3
,},
LI Qiang^{2, 3
,},
WANG Fei-Yue^{2, 4
,}

1.
Department of Automation, University of Science and Technology of China, Hefei 230027
2.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
3.
School of Artiflcial Intelligence, University of Chinese Academy of Sciences, Beijing 100049
4.
Qingdao Academy of Intelligent Industries, Qingdao 266000

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

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%.
- 平行点云 /
- 虚实互动 /
- 三维视觉模型 /
- 三维目标检测
Abstract: The extraction of 3D information is playing an increasingly important role in intelligent traffic scenes such as autonomous driving. In order to solve the problems faced by LiDAR sensor such as the high cost and incomplete coverage of possible scenarios, this paper proposes parallel point clouds and its framework. For parallel point clouds, virtual point clouds are obtained by building artiflcial scenes. Then 3D models are trained through computational experiments and tested by parallel execution. The evaluation results are fed back to the data generation and the training process of 3D models. Through continuous iteration, 3D models can be fully evaluated and updated. Under the framework of Parallel Point Clouds, we take the 3D object detection as an example and build a point clouds dataset in a closed-loop manner. Without human annotation, it can be used to effectively train the detection model which can achieve the 72% of the performance of model trained with annotated data.
- Parallel point clouds /
- virtual-real interaction /
- 3D vision models /
- 3D object detection

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图 1 平行点云框架

Fig. 1 The framework of parallel point clouds

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图 2 激光雷达扫描示意图

Fig. 2 The illustration of LiDAR scanning

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图 3 CARLA仿真环境及虚拟雷达点云

Fig. 3 CARLA simulation environment and virtual LiDAR point clouds

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图 4 KITTI伪雷达点云生成方法

Fig. 4 Pesudo LiDAR point clouds generation with KITTI dataset

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图 5 虚实结合的点云生成方法

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

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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)

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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

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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

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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

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图 10 KITTI数据集中的长尾分布

Fig. 10 The long-tailed distribution in KITTI dataset

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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

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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)	85.21	75.86	69.21
3D AP (Ours)	71.44	54.39	52.30
BEV AP (Real)	89.79	87.44	84.77
BEV AP (Ours)	82.81	70.44	65.68
AOS AP (Real)	90.63	89.34	88.36
AOS AP (Ours)	84.02	69.65	68.08

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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)	87.43	76.48	69.10
3D AP (Ours)	59.67	41.18	37.67
BEV AP (Real)	89.52	87.28	83.89
BEV AP (Ours)	73.55	53.37	52.34
AOS AP (Real)	90.49	89.45	88.41
AOS AP (Ours)	75.01	54.72	54.13

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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
G0	10.64	10.32	10.53	仅对少数目标能有效检测	增加CAD模型种类	—
G1	22.14	16.46	13.98	目标尺寸估计欠佳	改进CAD模型尺寸	6.14 (G0 to G1)
G2	32.85	22.40	19.52	对稀疏目标检测效果欠佳	降低前景目标密度	5.94 (G1 to G2)
G3	29.43	26.85	23.51	目标高度估计效果欠佳	调整前景目标高度	4.45 (G2 to G3)
G4	38.99	29.74	27.10	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)
G5	39.22	31.04	30.30	—	—	2.30 (G4 to G5)

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