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检索条件"主题词=3D semantic segmentation"
98 条 记 录,以下是1-10 订阅
排序:
deep Learning on 3d semantic segmentation: A detailed Review
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REMOTE SENSING 2025年 第2期17卷 298-298页
作者: Betsas, Thodoris Georgopoulos, Andreas doulamis, Anastasios Grussenmeyer, Pierre Natl Tech Univ Athens Sch Rural Surveying & Geoinformat Engn Lab Photogrammetry Athens 15772 Greece Univ Strasbourg ICube Lab INSA Strasbourg CNRSUMR 7357 F-67084 Strasbourg France
In this paper, an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3d semantic segmentation (3dSS) is presented. In the related literature, the taxonomy scheme used for the cl... 详细信息
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Robust 3d semantic segmentation Method Based on Multi-Modal Collaborative Learning
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REMOTE SENSING 2024年 第3期16卷 453-453页
作者: Ni, Peizhou Li, Xu Xu, Wang Zhou, Xiaojing Jiang, Tao Hu, Weiming Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Xuzhou XCMG Automobile Mfg Co Ltd Xuzhou 221112 Peoples R China China Automot Engn Res Inst Co Ltd Chongqing 401122 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China
Since camera and LidAR sensors provide complementary information for the 3d semantic segmentation of intelligent vehicles, extensive efforts have been invested to fuse information from multi-modal data. despite consid... 详细信息
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EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3d semantic segmentation
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IEEE TRANSACTIONS ON PATTERN ANALYSIS ANd MACHINE INTELLIGENCE 2024年 第12期46卷 8258-8273页
作者: Tan, Mingkui Zhuang, Zhuangwei Chen, Sitao Li, Rong Jia, Kui Wang, Qicheng Li, Yuanqing South China Univ Technol Sch Software Engn Guangzhou 510641 Guangdong Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Math Clear Water Bay Hong Kong Peoples R China Minieye Shenzhen 518063 Guangdong Peoples R China
We study multi-sensor fusion for 3d semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods, however, may not achiev... 详细信息
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Robust 3d semantic segmentation Based on Multi-Phase Multi-Modal Fusion for Intelligent Vehicles
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年 第1期9卷 1602-1614页
作者: Ni, Peizhou Li, Xu Xu, Wang Kong, dong Hu, Yue Wei, Kun Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China
3d semantic segmentation is a key technology for intelligent vehicles. Recently, great efforts have been made to achieve accurate and robust 3d semantic segmentation results through LidAR-camera fusion, due to the com... 详细信息
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A Multi-Phase Camera-LidAR Fusion Network for 3d semantic segmentation With Weak Supervision
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IEEE TRANSACTIONS ON CIRCUITS ANd SYSTEMS FOR VIdEO TECHNOLOGY 2023年 第8期33卷 3737-3746页
作者: Chang, Xuepeng Pan, Huihui Sun, Weichao Gao, Huijun Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Peoples R China Ningbo Inst Intelligent Equipment Technol Co Ltd Ningbo 315200 Peoples R China Yongjiang Lab Ningbo 315202 Peoples R China
Camera and LidAR are indispensable perception units in autonomous driving, providing complementary environmental information for 3d semantic segmentation. It is the key point that fuses the information of two modaliti... 详细信息
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OA-CNNs: Omni-Adaptive Sparse CNNs for 3d semantic segmentation
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentat...
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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Peng, Bohao Wu, Xiaoyang Jiang, Li Chen, Yukang Zhao, Hengshuang Tian, Zhuotao Jia, Jiaya CUHK Hong Kong Peoples R China HKU Hong Kong Peoples R China CUHK Shenzhen Peoples R China HIT Shenzhen Peoples R China
The booming of 3d recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especially in 3d semantic segmentation. Howe... 详细信息
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Open-Vocabulary 3d semantic segmentation with Foundation Models
Open-Vocabulary 3D Semantic Segmentation with Foundation Mod...
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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Jiang, Li Shi, Shaoshuai Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany
In dynamic 3d environments, the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need, we introduce OV3... 详细信息
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CLFusion:3d semantic segmentation Based on Camera and Lidar Fusion
CLFusion:3D Semantic Segmentation Based on Camera and Lidar ...
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IEEE International Symposium on Circuits and Systems (ISCAS)
作者: Wang, Tianyue Song, Rujun Xiao, Zhuoling Yan, Bo Qin, Haojie He, di Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China
In the field of autonomous driving, semantic segmentation is crucial for scene understanding. Currently, there are two main methods: camera-based and Lidar-based approaches. To address the issues of Lidar segmentation... 详细信息
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ROBUST 3d semantic segmentation WITH INCOMPLETE POINT CLOUdS BASEd ON SEQUENTIAL FRAME SAMPLING  31
ROBUST 3D SEMANTIC SEGMENTATION WITH INCOMPLETE POINT CLOUDS...
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2024 International Conference on Image Processing
作者: Yamaguchi, Masahiro Higa, Kyota Hosoi, Toshinori Shibata, Takashi NEC Corp Ltd Visual Intelligence Res Labs Shimonumabe 1753Nakahara Ku Kawasaki Kanagawa Japan
This paper proposes a method for learning 3d semantic segmentation robust to incomplete point clouds. Our method first generates pseud-incomplete point clouds from original 3d point clouds by sequential frame sampling... 详细信息
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Adaptive Margin Contrastive Learning for Ambiguity-aware 3d semantic segmentation
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D ...
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IEEE International Conference on Multimedia and Expo (ICME)
作者: Chen, Yang duane, Yueqi Zhang, Runzhong Tan, Yap-Peng Nanyang Technol Univ Singapore Singapore Tsinghua Univ Beijing Peoples R China
In this paper, we propose an adaptive margin contrastive learning method for 3d point cloud semantic segmentation, namely AMContrast3d. Most existing methods use equally penalized objectives, which ignore per-point am... 详细信息
来源: 评论