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{
            
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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Xuehao Wang",
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  "author": "Xuehao Wang",
            
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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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    "https://doi.org/10.48550/arXiv.2403.10971"
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    "https://doi.org/10.48550/arXiv.2403.10971"
            
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  ],
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  "defined_in": "https://doi.org/10.1007/978-3-030-01225-0_11",
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  "defined_in": "https://doi.org/10.1007/978-3-030-01225-0_11",
            
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  "doi": "10.57702/2lkei111",
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  "doi": "10.57702/2lkei111",
            
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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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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Feiyang Ye",
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      "extra_author": "Feiyang Ye",
            
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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 Zhang",
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      "extra_author": "Yu Zhang",
            
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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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      "name": "depth-estimation",
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      "name": "depth-estimation",
            
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      "title": "Depth Estimation"
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      "title": "Depth Estimation"
            
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      "name": "image-segmentation",
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      "name": "image-segmentation",
            
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      "title": "Image Segmentation"
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      "title": "Image Segmentation"
            
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    },
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      "description": "",
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      "name": "indoor-scene-understanding",
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      "name": "indoor-scene-understanding",
            
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      "title": "Indoor Scene Understanding"
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      "title": "Indoor Scene Understanding"
            
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    }
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  "landing_page": "https://www.cs.cmu.edu/~ronny/nyu-depth-v2/",
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  "landing_page": "https://www.cs.cmu.edu/~ronny/nyu-depth-v2/",
            
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  "license_title": null,
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  "metadata_created": "2024-12-02T22:47:42.528951",
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  "metadata_modified": "2024-12-02T22:47:43.129229",
            
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  "name": "nyuv2-dataset",
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  "name": "nyuv2-dataset",
            
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  "notes": "The NYUv2 dataset consists of video sequences of various 
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  "notes": "The NYUv2 dataset consists of video sequences of various 
            
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indoor scenes recorded by RGB and Depth cameras in Microsoft Kinect. 
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indoor scenes recorded by RGB and Depth cameras in Microsoft Kinect. 
            
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It contains 1,449 images with ground truth, where 795 images are for 
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It contains 1,449 images with ground truth, where 795 images are for 
            
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training and 654 images are for validation.",
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training and 654 images are for validation.",
            
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    "approval_status": "approved",
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      "created": "2024-12-02T22:29:38",
            
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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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      "id": "3c08a798-cec3-4682-a668-4f95d6d8ad18",
            
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      "name": "depth estimation",
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      "name": "depth estimation",
            
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  "title": "NYUv2 Dataset",
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  "title": "NYUv2 Dataset",
            
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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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}