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Article
Engineering, Electrical & Electronic
Wenjun Shi et al.
Summary: This paper presents a methodology for obtaining 3D semantic maps from RGB-D scans, focusing on accurate 2D frame labeling and semantic fusion. The proposed method achieves steady semantic segmentation through a two-stream network and introduces a discriminatory mask loss. The method also ensures spatial continuity and spatiotemporal consistency by using a label-oriented voxelgrid filter and an adaptive grouping algorithm.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
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Puzuo Wang et al.
Summary: In this study, a deep-learning-based weakly supervised framework is proposed for the semantic segmentation of ALS point clouds. The method utilizes implicit information from unlabeled data with incomplete and sparse labels, and introduces entropy regularization, consistency constraint, and an online soft pseudo-labeling strategy to improve classification performance.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Mengqi Rong et al.
Summary: In this paper, an active learning based 3D semantic labeling method is proposed to generate accurate 3D semantic mesh models by integrating 2D semantic segmentation results and 3D mesh models. Through the iterative process of training, fusion, and selection, the labeling quality is improved while reducing the amount of annotation needed.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Theory & Methods
Pengzhen Ren et al.
Summary: Researchers have shown relatively lower interest in active learning compared to deep learning, but with the increasing demand for large-scale high-quality annotated datasets, active learning is receiving more attention. This article provides a comprehensive survey on deep active learning, including a formal classification method, an overview of existing work, and an analysis of developments from an application perspective.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Artificial Intelligence
Yulan Guo et al.
Summary: Recently, deep learning on point clouds has been gaining more attention, but it is still in its early stages due to the unique challenges faced in processing point clouds with deep neural networks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Geography, Physical
Weixiao Gao et al.
Summary: Recent advancements in data acquisition technology have enabled rapid collection of 3D texture meshes, aiding in urban environment analysis and planning. Semantic segmentation through deep learning enhances understanding but demands a significant amount of labelled data. This research introduces a new benchmark dataset, semi-automatic annotation framework, and annotation tool for 3D meshes, offering potential time savings and comparative analysis for semantic segmentation methods.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Gyungin Shin et al.
Summary: The study demonstrates that good segmentation performance can be achieved with only a few well-chosen pixel labels, and proposes an active learning framework to significantly reduce labeling cost. Extensive experiments show that our approach performs comparably with existing techniques in low annotation regimes.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Qingyong Hu et al.
Summary: This paper introduces an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which covers large areas from three UK cities. The performance of state-of-the-art algorithms on this dataset is extensively evaluated, and a comprehensive analysis of the results is provided, identifying key challenges towards urban-scale point cloud understanding.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
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Computer Science, Software Engineering
Alexandre Boulch et al.
COMPUTERS & GRAPHICS-UK
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Andrea Romanoni et al.
2018 INTERNATIONAL CONFERENCE ON 3D VISION (3DV)
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Felix Jaremo Lawin et al.
COMPUTER ANALYSIS OF IMAGES AND PATTERNS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Maros Blaha et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Angela Dai et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
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Engineering, Electrical & Electronic
Antonio Torralba et al.
PROCEEDINGS OF THE IEEE
(2010)