Semantic Segmentation of In-Vehicle Point Cloud With Improved RangeNet++ Loss Function

Semantic Segmentation of In-Vehicle Point Cloud With Improved RangeNet++ Loss Function

To solve the problem of inaccurate object segmentation caused by unbalanced samples for in-vehicle point cloud, an improved semantic segmentation network RangeNet++ based on asymmetric loss function (AsL-RangeNet++) is proposed, which uses asymmetric loss...

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Bibliographic Details
Main Authors: Jinhui Zhang, Haiyan Jiang, Huizhi Shao, Qingjun Song, Xiaoshuang Wang, Dashuai Zong
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Semantic segmentation
rangenet++
asymmetric loss function
in-vehicle point cloud
Online Access:https://ieeexplore.ieee.org/document/10023475/

Internet

https://ieeexplore.ieee.org/document/10023475/

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