WE2.R3.1

A DEEP ACTIVE LEARNING FRAMEWORK FOR SEMANTIC LABELING OF POINT CLOUDS

Hongjing Wu, Huan Luo, Wenzhong Guo, Fuzhou University, China
Session:
WE2.R3: LiDAR Point Clouds Intelligent Processing Oral
Track:
Community-Contributed Sessions
Location:
Room 107
Presentation Time:
Wed, 19 Jul, 10:15 - 10:27 Pacific Time (UTC -7)
Session Co-Chairs:
Yiping Chen, Sun Yat-sen University and Jonathan Li, University of Waterloo
Session Manager:
HUMAIRA SANAM
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Session WE2.R3
WE2.R3.1: A DEEP ACTIVE LEARNING FRAMEWORK FOR SEMANTIC LABELING OF POINT CLOUDS
Hongjing Wu, Huan Luo, Wenzhong Guo, Fuzhou University, China
WE2.R3.2: GRAPH NEURAL NETWORK WITH MULTI-KERNEL LEARNING FOR MULTISPECTRAL POINT CLOUD CLASSIFICATION
Zifeng Zhang, Qingwang Wang, Mingye Wang, Tao Shen, Kunming University of Science and Technology, China
WE2.R3.3: TREE SPECIES CLASSIFICATION USING DEEP LEARNING BASED 3D POINT CLOUD TRANSFORMER ON AIRBORNE LIDAR DATA
Lanying Wang, Dening Lu, Weikai Tan, University of Waterloo, Canada; Haiyan Guan, Nanjing University of Information Science and Technology, China; Yiping Chen, Sun Yat-sen University, China; Jonathan Li, University of Waterloo, Canada
WE2.R3.4: INVESTIGATING THE IMPACT OF POINT CLOUD DENSITY ON SEMANTIC SEGMENTATION PERFORMANCE USING VIRTUAL LIDAR IN BOREAL FOREST
Olivier Stocker, Reza Mahmoudi Kouhi, Eric Guilbert, Laval University, Canada; Antonio Ferraz, NASA, Canada; Thierry Badard, Laval University, Canada
WE2.R3.5: Optimal Transport for Change Detection on LiDAR Point Clouds
Marco Fiorucci, Center for Cultural Heritage Technology, Istituto Italiano di Tecnologia, Kyoto University, Italy; Peter Naylor, RIKEN Center for Advanced Intelligence Project (AIP), Japan; Makoto Yamada, Kyoto University, RIKEN Center for Advanced Intelligence Project (AIP), Okinawa Institute of Science and Technology, Japan
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