Institute for Information Industry (III) Research Report 2018/09/25 黃季軒 0935090120 tazdingoHuang@gmail.com

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

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

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

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.

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%

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

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

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

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

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

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.

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

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

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

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%

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%

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%

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%

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%