Linking Points with Labels in 3D: A Review of Point Cloud Semantic Segmentation

Yuxing Xie; Jiaojiao Tian; Xiao Xiang Zhu

doi:10.1109/MGRS.2019.2937630

Linking Points with Labels in 3D: A Review of Point Cloud Semantic Segmentation

Yuxing Xie
, Jiaojiao Tian
, Xiao Xiang Zhu

Deutsches Zentrum für Luft- und Raumfahrt (DLR)

Research output: Contribution to journal › Review article › peer-review

414 Scopus citations

Abstract

Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.

Original language	English
Article number	9028090
Pages (from-to)	38-59
Number of pages	22
Journal	IEEE Geoscience and Remote Sensing Magazine
Volume	8
Issue number	4
DOIs	https://doi.org/10.1109/MGRS.2019.2937630
State	Published - Dec 2020
Externally published	Yes

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10.1109/MGRS.2019.2937630

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title = "Linking Points with Labels in 3D: A Review of Point Cloud Semantic Segmentation",

abstract = "Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.",

author = "Yuxing Xie and Jiaojiao Tian and Zhu, \{Xiao Xiang\}",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2020",

month = dec,

doi = "10.1109/MGRS.2019.2937630",

language = "English",

volume = "8",

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T1 - Linking Points with Labels in 3D

T2 - A Review of Point Cloud Semantic Segmentation

AU - Xie, Yuxing

AU - Tian, Jiaojiao

AU - Zhu, Xiao Xiang

PY - 2020/12

Y1 - 2020/12

N2 - Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.

AB - Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.

UR - https://www.scopus.com/pages/publications/85081356659

U2 - 10.1109/MGRS.2019.2937630

DO - 10.1109/MGRS.2019.2937630

M3 - Review article

AN - SCOPUS:85081356659

SN - 2473-2397

VL - 8

SP - 38

EP - 59

JO - IEEE Geoscience and Remote Sensing Magazine

JF - IEEE Geoscience and Remote Sensing Magazine

IS - 4

M1 - 9028090

ER -

Linking Points with Labels in 3D: A Review of Point Cloud Semantic Segmentation

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