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Deep Learning for 3D Point Clouds: A Survey

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.12 December 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4338-4364

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENTUK GALAKSI
OBJECT TRACKING
SHAPE CLASSIFICATION
OBJECT DETECTION
SCENE FLOW
SEMANTIC SEGMENTATION
3D DATA
POINT CLOUDS
SHAPE RETRIEVAL
INSTANCE SEGMENTATION
PART SEGMENTATION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3005434

Pernyataan Tanggungjawab

Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, Mohammed Bennamoun

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