Label-efficient semantic segmentation of large-scale industrial point clouds using weakly supervised learning-Reference-Cited by-同舟云学术

Label-efficient semantic segmentation of large-scale industrial point clouds using weakly supervised learning

Published:2023-04 Issue: Volume:148 Page:104757
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Yin Chao,Yang Bo,Cheng Jack C.P.,Gan Vincent J.L.,Wang Boyu,Yang Ji

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference72 articles.

1. Learning to Match 2D Images and 3D LiDAR Point Clouds for Outdoor Augmented Reality, in: 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW);Liu,2020

2. AdapNet: adaptive semantic segmentation in adverse environmental conditions;Valada,2017

3. SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences;Behley,2019

4. Are we ready for autonomous driving? The KITTI vision benchmark suite;Geiger,2012

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