TY - GEN
T1 - Large Scale Road Datasets and Point-Offset Network for 3D Instance Segmentation
AU - Chen, Yuzhen
AU - Yang, Ying
AU - Lv, Jiajin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the field of autonomous driving, recognition and segmentation of road point clouds is an important task for the automatic production of 3D high-precision maps. To address the problems of lack of large-scale and complex road scene datasets for the instance segmentation, and the poor applicability of algorithms under large scenes, this paper produces a brand new and large-scale road instance segmentation dataset. Meanwhile, this paper proposes a brand new solution for semantic segmentation and clustering bias prediction, based on an improved Pointnet++ network, which is used together with the clustering algorithm of DBSCAN to conduct the instance segmentation. Thorough experiments indicate that the semantic segmentation accuracy of the proposed method reaches 0.982 on our produced road instance segmentation datasets, meanwhile the average accuracy and recall of the three classes of instance segmentation reach 0.853 and 0.784, respectively. Moreover, the bias network branch proposed in this paper can further improve the effectiveness of clustering, and the precision of our algorithm was improved by 15.1% and the recall rate was improved by 16.2%. It can be concluded that our produced dataset can support the large-scale road instance segmentation and our proposed algorithm can better adapt to the instance segmentation under large-scale and complex road scenarios.
AB - In the field of autonomous driving, recognition and segmentation of road point clouds is an important task for the automatic production of 3D high-precision maps. To address the problems of lack of large-scale and complex road scene datasets for the instance segmentation, and the poor applicability of algorithms under large scenes, this paper produces a brand new and large-scale road instance segmentation dataset. Meanwhile, this paper proposes a brand new solution for semantic segmentation and clustering bias prediction, based on an improved Pointnet++ network, which is used together with the clustering algorithm of DBSCAN to conduct the instance segmentation. Thorough experiments indicate that the semantic segmentation accuracy of the proposed method reaches 0.982 on our produced road instance segmentation datasets, meanwhile the average accuracy and recall of the three classes of instance segmentation reach 0.853 and 0.784, respectively. Moreover, the bias network branch proposed in this paper can further improve the effectiveness of clustering, and the precision of our algorithm was improved by 15.1% and the recall rate was improved by 16.2%. It can be concluded that our produced dataset can support the large-scale road instance segmentation and our proposed algorithm can better adapt to the instance segmentation under large-scale and complex road scenarios.
UR - http://www.scopus.com/inward/record.url?scp=85138699541&partnerID=8YFLogxK
U2 - 10.1109/RCAR54675.2022.9872257
DO - 10.1109/RCAR54675.2022.9872257
M3 - Conference contribution
AN - SCOPUS:85138699541
T3 - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
SP - 534
EP - 539
BT - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
Y2 - 17 July 2022 through 22 July 2022
ER -