1. Institute for Information Industry (III) Research Report
2018/09/25
黃季軒

2. Urban Scene Point Cloud Segmentation
Road Surface Points Segmentation
Road Points
Non-road Points
Road Markings
Detection
Pole-like Objects Segmentation and Classification

3. Road Surface Points Segmentation
Input: original point cloud.
Output: road points.

4. Get 4 Features of Each Point
Method
Point Cloud
Get 4 Features of Each Point
Classify Road Points and Non-road Points Using Random Forest Classifier
Road Points
Non-road Points

5. Method
Features used in the classification are related to the eigenvalues and eigenvectors obtained from a principal component analysis (PCA) applied to the coordinates of the neighbors of each point of the cloud.
Four features are used:
Linearity: How elongated the neighborhood is.
Planarity: How well it is fitted by a plane.
Scattering: High scattering values correspond to an isotropic and spherical neighborhood.
Verticality: How vertical the neighborhood is.

6. Results
Reference: S. Guinard and L. Landrieu. Weakly supervised segmentation-aided classification of urban scenes from 3d LiDAR point clouds. In ISPRS 2017, 2017.
Precision
94.8%
Recall (road)
97.7%
Recall (non-road)
87.0%

7. Road Markings Detection
Input: road points.
Output: vectors of lines, bounding boxes of road markings.

8. Method Extract high reflectivity points in ground points.
Classify connected components by their region properties.

9. Results
: Road markings.
: Centerlines.
: Turning lines.

10. Pole-like Objects Segmentation and Classification
Input:
Urban scene non-road points.
Output:
Segmented pole-like objects.
Original point cloud scene.
Segmented objects.

12. Method Non-road Points Segment Pole-like Objects in Point Cloud
Get 4 Features of Each Pole-like Object
Classify Pole-like Object Using PointNet
Classify Pole-like Object Using Random Forest Classifier
Pole-like Objects’ Classes

13. Segment Pole-like Objects in Point Cloud
Voxelize the point cloud.
Locate isolated objects with a small horizontal section.
Consider vertical continuity and minimum height.
Get connected component.

14. Method (Random Forest)
PCA is used.
Four features are used:
Linearity: How elongated the neighborhood is.
Planarity: How well it is fitted by a plane.
Scattering: High scattering values correspond to an isotropic and spherical neighborhood.
Height

15. Method (PointNet) Suitable for consuming unordered point sets in 3D.
Capture local structures from nearby points.
Invariance under transformations.

16. Pole-like Objects A B C D data set Pedestrian Signal 103 Street Light
traffic light
traffic sign
Pedestrian Signal
103
Street Light
282
Traffic Light
167
Traffic Sign 35
data set

17. Classification Results (Dataset 1)
Random Forest
Point Net
Class
Precision
Recall
Pedestrian Signal
70%
92%
Street light
95%
90%
Traffic Light
97%
71%
Traffic Sign
25%
44%
Class
Precision
Recall
Pedestrian Signal
81%
68%
Street light
90%
95%
Traffic Light
94%
100%
Traffic Sign
43%
38%
Over all accuracy: 82%
Over all accuracy: 88%
Average accuracy: 72%
Average accuracy: 77%

18. Classification Results (Dataset 2)
Random Forest
Point Net
Class
Precision
Recall
Pedestrian Signal
78%
81%
Street light
93%
96%
Traffic Light
95%
Traffic Sign
86%
67%
Class
Precision
Recall
Pedestrian Signal
100%
Street light
98%
Traffic Light
97%
Traffic Sign
Over all accuracy: 90%
Over all accuracy: 99%
Average accuracy: 88%
Average accuracy: 99%

19. Classification Results (Dataset 3)
Random Forest
Point Net
Class
Precision
Recall
Pedestrian Signal
67%
62%
Street light
84%
90%
Traffic Light
93%
100%
Traffic Sign
22%
Class
Precision
Recall
Pedestrian Signal
83%
91%
Street light
95%
Traffic Light
100%
92%
Traffic Sign
43%
Over all accuracy: 84%
Over all accuracy: 91%
Average accuracy: 78%
Average accuracy: 80%

20. Classification Results (Dataset 4)
Random Forest
Point Net
Class
Precision
Recall
Pedestrian Signal
80%
62%
Street light
88%
93%
Traffic Light
87%
95%
Traffic Sign
50%
38%
Class
Precision
Recall
Pedestrian Signal
100%
76%
Street light
97%
98%
Traffic Light
89%
Traffic Sign
33%
50%
Over all accuracy: 85%
Over all accuracy: 92%
Average accuracy: 76%
Average accuracy: 80%

21. Classification Results
Random Forest
Point Net
Over all accuracy
85.25%
92.50%
Average accuracy
78.50%
84.00%
Average recall
74.75%
87.13%