3D目标检测跟踪 | 基于kitti+waymo数据集的自动驾驶场景的3D目标检测+跟踪渲染可视化

项目应用场景

- 面向自动驾驶场景的 3D 目标检测+目标跟踪，基于kitti+waymo数据集的自动驾驶场景的3D目标检测+跟踪渲染可视化查看。

项目效果

项目细节 ==> 具体参见项目 README.md

- (1) Kitti detection 数据集结构

# For Kitti Detection Dataset         
└── kitti_detection├── testing |      ├──calib|      ├──image_2|      ├──label_2|      └──velodyne      └── training├──calib├──image_2├──label_2└──velodyne

- (2) Kitti tracking 数据集结构

# For Kitti Tracking Dataset         
└── kitti_tracking├── testing |      ├──calib|      |    ├──0000.txt|      |    ├──....txt|      |    └──0028.txt|      ├──image_02|      |    ├──0000|      |    ├──....|      |    └──0028|      ├──label_02|      |    ├──0000.txt|      |    ├──....txt|      |    └──0028.txt|      └──velodyne|           ├──0000|           ├──....|           └──0028      └── training # the structure is same as testing set├──calib├──image_02├──label_02└──velodyne

- (3) 安装依赖

pip install python3 \numpy==1.21.3 \vedo==2021.0.6 \vtk==9.0.3 \opencv==4.5.4.58 \matplotlib==3.4.3

- (4) 执行示例

from viewer.viewer import Viewer
import numpy as npvi = Viewer() # set box_type='OpenPCDet' if you use OpenPCDet boxes
len_dataset = 1000for i in range(len_dataset):pseudo_boxes = np.array([[i*0.05, -1, 1, 1, 1, 1, 0], [i*0.05, 1, 1, 1, 1, 1, 0]]) # your boxesids = np.array([0,1]) # your boxes ids (optional)pseudo_points = np.random.randn(100, 3) # your pointsvi.add_points(pseudo_points, radius=4, scatter_filed=pseudo_points[:, 0])vi.add_3D_boxes(pseudo_boxes, ids=ids,caption_size=(0.09,0.09))vi.add_spheres(pseudo_boxes[:, 0:3],radius=0.03,res=10,color='red',del_after_show=False, alpha=1) # Draw motion trackvi.show_3D() # press the Q or Enter or ESC key to view