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 np

vi = Viewer() # set box_type='OpenPCDet' if you use OpenPCDet boxes
len_dataset = 1000

for 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 boxes
    ids = np.array([0,1]) # your boxes ids (optional)

    pseudo_points = np.random.randn(100, 3) # your points

    vi.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 track
    vi.show_3D() # press the Q or Enter or ESC key to view