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{
            
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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Renrui Zhang",
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  "author": "Renrui Zhang",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "citation": [],
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "",
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  "defined_in": "",
            
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  "doi": "10.57702/jvwgg65m",
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  "doi": "10.57702/jvwgg65m",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-02",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "doi_status": false,
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  "doi_status": true,
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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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": "Ziyu Guo",
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      "extra_author": "Ziyu Guo",
            
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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": "Wei Zhang",
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      "extra_author": "Wei Zhang",
            
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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": "Kunchang Li",
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      "extra_author": "Kunchang 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": "Xupeng Miao",
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      "extra_author": "Xupeng Miao",
            
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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": "Bin Cui",
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      "extra_author": "Bin Cui",
            
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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": "Yu Qiao",
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      "extra_author": "Yu Qiao",
            
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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": "Peng Gao",
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      "extra_author": "Peng Gao",
            
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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": "Hongsheng Li",
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      "extra_author": "Hongsheng 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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  "groups": [
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  "groups": [
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "3D Object Recognition",
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      "display_name": "3D Object Recognition",
            
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      "id": "fb966b89-2ab8-4ac3-ae8e-55e18c4de9e1",
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "3d-object-recognition",
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      "name": "3d-object-recognition",
            
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      "title": "3D Object Recognition"
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      "title": "3D Object Recognition"
            
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    }
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    }
            
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  ],
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  ],
            
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  "id": "af5d4902-3725-47e8-a3c2-3030958f02dd",
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  "id": "af5d4902-3725-47e8-a3c2-3030958f02dd",
            
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  "isopen": false,
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  "isopen": false,
            
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  "landing_page": "https://github.com/ZrrSkywalker/PointCLIP",
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  "landing_page": "https://github.com/ZrrSkywalker/PointCLIP",
            
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  "license_title": null,
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  "license_title": null,
            
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  "link_orkg": "",
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  "link_orkg": "",
            
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  "metadata_created": "2024-12-02T23:31:38.072684",
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  "metadata_created": "2024-12-02T23:31:38.072684",
            
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  "metadata_modified": "2024-12-02T23:31:38.072690",
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  "metadata_modified": "2024-12-02T23:31:38.791706",
            
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  "name": "modelnet10--modelnet40--and-scanobjectnn",
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  "name": "modelnet10--modelnet40--and-scanobjectnn",
            
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  "notes": "The dataset used in the PointCLIP paper is ModelNet10, 
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  "notes": "The dataset used in the PointCLIP paper is ModelNet10, 
            
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ModelNet40, and ScanObjectNN. ModelNet10 consists of 4,899 synthetic 
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ModelNet40, and ScanObjectNN. ModelNet10 consists of 4,899 synthetic 
            
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meshed CAD models with 10 indoor categories, 3,991 for training and 
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meshed CAD models with 10 indoor categories, 3,991 for training and 
            
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908 for testing. ModelNet40 is larger and contains 12,311 samples of 
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908 for testing. ModelNet40 is larger and contains 12,311 samples of 
            
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40 common categories, 9,843 for training and 2,468 for testing. 
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40 common categories, 9,843 for training and 2,468 for testing. 
            
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ScanObjectNN contains 2,321 training and 581 testing point clouds of 
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ScanObjectNN contains 2,321 training and 581 testing point clouds of 
            
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15 categories collected directly from real-world scans.",
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15 categories collected directly from real-world scans.",
            
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  "num_tags": 3,
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  "organization": {
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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  "relationships_as_object": [],
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      "created": "2024-12-03T00:20:35",
            
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      "data": [
            
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      "description": "The json representation of the dataset with its 
            
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distributions based on DCAT.",
            
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      "format": "JSON",
            
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      "hash": "",
            
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      "id": "69177b7a-b481-4b9b-bb6e-8e0910a26f97",
            
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  "state": "active",
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  "state": "active",
            
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  "tags": [
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  "tags": [
            
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    {
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    {
            
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      "display_name": "3D Object Recognition",
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      "display_name": "3D Object Recognition",
            
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      "name": "3D Object Recognition",
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      "name": "3D Object Recognition",
            
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      "state": "active",
            
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      "display_name": "CAD Models",
            
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      "name": "CAD Models",
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    },
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    {
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    {
            
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      "display_name": "Point Clouds",
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      "display_name": "Point Clouds",
            
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      "id": "cd5e5c68-6deb-4e83-b810-0756f3961446",
            
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      "name": "Point Clouds",
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      "name": "Point Clouds",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "ModelNet10, ModelNet40, and ScanObjectNN",
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  "title": "ModelNet10, ModelNet40, and ScanObjectNN",
            
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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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}