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  "access_rights": "",
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  "author": "Rui Yu",
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  "author": "Rui Yu",
            
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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.2402.09944",
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    "https://doi.org/10.48550/arXiv.2402.09944",
            
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    "https://doi.org/10.48550/arXiv.2309.13240"
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  ],
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-02",
            
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    {
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      "extra_author": "Jiachen Liu",
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      "extra_author": "Jiachen Liu",
            
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    },
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      "extra_author": "Zihan Zhou",
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    },
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    {
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      "extra_author": "Sharon X. Huang",
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      "extra_author": "Sharon X. Huang",
            
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      "name": "indoor-scenes",
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      "name": "indoor-scenes",
            
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      "title": "Indoor Scenes"
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      "title": "Indoor Scenes"
            
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      "description": "",
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      "name": "semantic-segmentation",
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      "name": "semantic-segmentation",
            
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      "title": "Semantic Segmentation"
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      "title": "Semantic Segmentation"
            
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  "name": "scannet-dataset",
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  "name": "scannet-dataset",
            
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  "notes": "The ScanNet dataset is a large-scale indoor dataset 
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  "notes": "The ScanNet dataset is a large-scale indoor dataset 
            
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composed of monocular sequences with ground truth poses and depth 
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composed of monocular sequences with ground truth poses and depth 
            
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      "name": "indoor scene understanding",
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      "name": "indoor scene understanding",
            
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    }
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    }
            
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  ],
            
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  "title": "ScanNet Dataset",
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  "title": "ScanNet 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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}