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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Tianpei Zou",
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  "author": "Tianpei Zou",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "defined_in": "https://doi.org/10.48550/arXiv.2407.12387",
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  "defined_in": "https://doi.org/10.48550/arXiv.2407.12387",
            
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  "doi": "10.57702/irlzamx3",
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  "doi": "10.57702/irlzamx3",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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  "doi_publisher": "TIB",
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Sanqing Qu",
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      "extra_author": "Sanqing Qu",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Zhijun Li",
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      "extra_author": "Zhijun Li",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Alois Knoll",
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      "extra_author": "Alois Knoll",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Lianghua He",
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      "extra_author": "Lianghua He",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Guang Chen",
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      "extra_author": "Guang Chen",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Changjun Jiang",
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      "extra_author": "Changjun Jiang",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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      "description": "",
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      "description": "",
            
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      "name": "point-cloud-segmentation",
            
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      "description": "",
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      "description": "",
            
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try-learning-for-test-time-adaptation-in-3d-point-cloud-segmentation",
            
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  "notes": "3D point cloud segmentation has received significant 
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  "notes": "3D point cloud segmentation has received significant 
            
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interest for its growing applications. However, the generalization 
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interest for its growing applications. However, the generalization 
            
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ability of models suffers in dynamic scenarios due to the distribution 
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ability of models suffers in dynamic scenarios due to the distribution 
            
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shift between test and training data.",
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shift between test and training data.",
            
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      "display_name": "3D point cloud",
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      "display_name": "3D point cloud",
            
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      "name": "segmentation",
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      "display_name": "test-time adaptation",
            
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      "name": "test-time adaptation",
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      "name": "test-time adaptation",
            
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      "vocabulary_id": null
            
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    }
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  ],
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  ],
            
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  "title": "HGL: Hierarchical Geometry Learning for Test-time 
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  "title": "HGL: Hierarchical Geometry Learning for Test-time 
            
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Adaptation in 3D Point Cloud Segmentation",
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Adaptation in 3D Point Cloud Segmentation",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}