Instance Segmentation of LiDAR Point Clouds

Feihu Zhang; Chenye Guan; Jin Fang; Song Bai; Ruigang Yang; Philip H.S. Torr; Victor Prisacariu

doi:10.1109/ICRA40945.2020.9196622

Instance Segmentation of LiDAR Point Clouds

Feihu Zhang
, Chenye Guan
, Jin Fang
, Song Bai
, Ruigang Yang
, Philip H.S. Torr
, Victor Prisacariu

Computer Science

Producción científica: Conference contribution › revisión exhaustiva

63 Citas (Scopus)

Resumen

We propose a robust baseline method for instance segmentation which are specially designed for large-scale outdoor LiDAR point clouds. Our method includes a novel dense feature encoding technique, allowing the localization and segmentation of small, far-away objects, a simple but effective solution for single-shot instance prediction and effective strategies for handling severe class imbalances. Since there is no public dataset for the study of LiDAR instance segmentation, we also build a new publicly available LiDAR point cloud dataset to include both precise 3D bounding box and point-wise labels for instance segmentation, while still being about 3∼20 times as large as other existing LiDAR datasets. The dataset will be published at https://github.com/feihuzhang/LiDARSeg.

Idioma original	English
Título de la publicación alojada	2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Páginas	9448-9455
Número de páginas	8
ISBN (versión digital)	9781728173955
DOI	https://doi.org/10.1109/ICRA40945.2020.9196622
Estado	Published - may 2020
Evento	2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France Duración: may 31 2020 → ago 31 2020

Serie de la publicación

Nombre	Proceedings - IEEE International Conference on Robotics and Automation
ISSN (versión impresa)	1050-4729

Conference

Conference	2020 IEEE International Conference on Robotics and Automation, ICRA 2020
País/Territorio	France
Ciudad	Paris
Período	5/31/20 → 8/31/20

Nota bibliográfica

Publisher Copyright:
© 2020 IEEE.

Financiación

ACKNOWLEDGEMENT Research is mainly supported by Baidu’s Robotics and Auto-driving Lab, in part by the ERC grant ERC-2012-AdG 321162-HELIOS, EPSRC grant Seebibyte EP/M013774/1 and EPSRC/MURI grant EP/N019474/1. We would also like to acknowledge the Royal Academy of Engineering. Victor Adrian Prisacariu would like to thank the European Commission Project Multiple-actOrs Virtual Empathic CARegiver for the Elder (MoveCare).

Financiadores	Número del financiador
Multidisciplinary University Research Initiative	EP/N019474/1
Multidisciplinary University Research Initiative
Engineering and Physical Sciences Research Council	EP/M013774/1
Engineering and Physical Sciences Research Council

ASJC Scopus subject areas

Software
Control and Systems Engineering
Electrical and Electronic Engineering
Artificial Intelligence

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10.1109/ICRA40945.2020.9196622

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@inproceedings{e155997350884279ba0bff2df247d703,

title = "Instance Segmentation of LiDAR Point Clouds",

abstract = "We propose a robust baseline method for instance segmentation which are specially designed for large-scale outdoor LiDAR point clouds. Our method includes a novel dense feature encoding technique, allowing the localization and segmentation of small, far-away objects, a simple but effective solution for single-shot instance prediction and effective strategies for handling severe class imbalances. Since there is no public dataset for the study of LiDAR instance segmentation, we also build a new publicly available LiDAR point cloud dataset to include both precise 3D bounding box and point-wise labels for instance segmentation, while still being about 3∼20 times as large as other existing LiDAR datasets. The dataset will be published at https://github.com/feihuzhang/LiDARSeg.",

author = "Feihu Zhang and Chenye Guan and Jin Fang and Song Bai and Ruigang Yang and Torr, \{Philip H.S.\} and Victor Prisacariu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 ; Conference date: 31-05-2020 Through 31-08-2020",

year = "2020",

month = may,

doi = "10.1109/ICRA40945.2020.9196622",

language = "English",

series = "Proceedings - IEEE International Conference on Robotics and Automation",

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booktitle = "2020 IEEE International Conference on Robotics and Automation, ICRA 2020",

}

Zhang, F, Guan, C, Fang, J, Bai, S, Yang, R, Torr, PHS & Prisacariu, V 2020, Instance Segmentation of LiDAR Point Clouds. En 2020 IEEE International Conference on Robotics and Automation, ICRA 2020., 9196622, Proceedings - IEEE International Conference on Robotics and Automation, pp. 9448-9455, 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, 5/31/20. https://doi.org/10.1109/ICRA40945.2020.9196622

TY - GEN

T1 - Instance Segmentation of LiDAR Point Clouds

AU - Zhang, Feihu

AU - Guan, Chenye

AU - Fang, Jin

AU - Bai, Song

AU - Yang, Ruigang

AU - Torr, Philip H.S.

AU - Prisacariu, Victor

PY - 2020/5

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AB - We propose a robust baseline method for instance segmentation which are specially designed for large-scale outdoor LiDAR point clouds. Our method includes a novel dense feature encoding technique, allowing the localization and segmentation of small, far-away objects, a simple but effective solution for single-shot instance prediction and effective strategies for handling severe class imbalances. Since there is no public dataset for the study of LiDAR instance segmentation, we also build a new publicly available LiDAR point cloud dataset to include both precise 3D bounding box and point-wise labels for instance segmentation, while still being about 3∼20 times as large as other existing LiDAR datasets. The dataset will be published at https://github.com/feihuzhang/LiDARSeg.

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M3 - Conference contribution

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ER -

Instance Segmentation of LiDAR Point Clouds

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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