TY - GEN
T1 - Point Cloud Semantic Segmentation Algorithm Based on Multi-information Markov Random Field
AU - Sun, Weizhen
AU - Guo, Jielong
AU - Wang, Zeli
AU - Fang, Li
AU - Li, Yuanxiang
AU - Wang, Fengxiang
AU - Wei, Xian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - There is a strong spatial relationship between point clouds obtained from the same object, nevertheless it usually takes a great quantity of time to directly establish the relationship model between individual point clouds. In this paper, we draw on this idea and propose a new semantic segmentation method based on 3D mesh model, which is applied to do the semantic segmentation of point cloud data in traffic scene. Firstly, the Markov random field is used to model according to the attribute information of the triangular patches of the 3D mesh model and the spatial dependence between the bins. Secondly, the attribute information of the triangle patch is represented by the intensity information of the point cloud data included in each triangle patch, and it is clustered by Gaussian Mixture model which describes the matching degree of each attribute with each class. The algorithm combines topology information of grid model and intensity information of point cloud data, eliminates the over segmentation effectively and makes the boundary of the partition smooth. Finally, ISDF (improved shape and diameter function) is proposed to determine the final class of two side triangular patches on the boundary. The proposed method is evaluated on two public point cloud datasets, and shows the competitive performance.
AB - There is a strong spatial relationship between point clouds obtained from the same object, nevertheless it usually takes a great quantity of time to directly establish the relationship model between individual point clouds. In this paper, we draw on this idea and propose a new semantic segmentation method based on 3D mesh model, which is applied to do the semantic segmentation of point cloud data in traffic scene. Firstly, the Markov random field is used to model according to the attribute information of the triangular patches of the 3D mesh model and the spatial dependence between the bins. Secondly, the attribute information of the triangle patch is represented by the intensity information of the point cloud data included in each triangle patch, and it is clustered by Gaussian Mixture model which describes the matching degree of each attribute with each class. The algorithm combines topology information of grid model and intensity information of point cloud data, eliminates the over segmentation effectively and makes the boundary of the partition smooth. Finally, ISDF (improved shape and diameter function) is proposed to determine the final class of two side triangular patches on the boundary. The proposed method is evaluated on two public point cloud datasets, and shows the competitive performance.
KW - 3D mesh model
KW - point cloud
KW - triangular face patch
UR - https://www.scopus.com/pages/publications/85062787456
U2 - 10.1109/SSCI.2018.8628817
DO - 10.1109/SSCI.2018.8628817
M3 - 会议稿件
AN - SCOPUS:85062787456
T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
SP - 287
EP - 294
BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
A2 - Sundaram, Suresh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Y2 - 18 November 2018 through 21 November 2018
ER -