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  "access_rights": "",
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  "author": "Zhiyang Wang",
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  "author": "Zhiyang Wang",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2002.03281",
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    "https://doi.org/10.48550/arXiv.2002.03281",
            
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    "https://doi.org/10.48550/arXiv.1711.08241",
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    "https://doi.org/10.48550/arXiv.1711.08241",
            
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    "https://doi.org/10.48550/arXiv.2306.04701",
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    "https://doi.org/10.48550/arXiv.2306.04701",
            
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    "https://doi.org/10.48550/arXiv.1804.04458"
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    "https://doi.org/10.48550/arXiv.1804.04458"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.2210.00376",
            
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  "doi": "10.57702/q6jhri4k",
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  "doi": "10.57702/q6jhri4k",
            
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  "doi_date_published": "2024-12-02",
            
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Luana Ruiz",
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      "extra_author": "Luana Ruiz",
            
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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": "Alejandro Ribeiro",
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      "extra_author": "Alejandro Ribeiro",
            
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      "orcid": ""
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      "orcid": ""
            
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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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      "name": "3d-models",
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      "name": "3d-models",
            
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      "title": "3D models"
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      "title": "3D models"
            
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      "name": "cad-models",
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      "name": "computer-vision",
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      "name": "computer-vision",
            
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      "title": "Computer Vision"
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      "title": "Computer Vision"
            
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      "description": "",
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      "name": "shape-segmentation",
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      "name": "shape-segmentation",
            
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      "title": "Shape segmentation"
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      "title": "Shape segmentation"
            
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  "landing_page": 
            
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"https://cs.nyu.edu/~ylclab/data/ShapeNetCoreV1.zip",
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"https://cs.nyu.edu/~ylclab/data/ShapeNetCoreV1.zip",
            
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  "license_title": null,
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  "name": "modelnet10",
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  "name": "modelnet10",
            
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  "notes": "3D Convolutional Neural Networks are sensitive to 
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  "notes": "3D Convolutional Neural Networks are sensitive to 
            
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transformations applied to their input. This is a problem because a 
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transformations applied to their input. This is a problem because a 
            
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voxelized version of a 3D object, and its rotated clone, will look 
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voxelized version of a 3D object, and its rotated clone, will look 
            
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unrelated to each other after passing through to the last layer of a 
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unrelated to each other after passing through to the last layer of a 
            
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network. Instead, an idealized model would preserve a meaningful 
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network. Instead, an idealized model would preserve a meaningful 
            
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representation of the voxelized object, while explaining the 
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representation of the voxelized object, while explaining the 
            
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pose-difference between the two inputs.",
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pose-difference between the two inputs.",
            
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      "id": "f650b4e3-9955-49b0-ba7b-2d302a990978",
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      "id": "f650b4e3-9955-49b0-ba7b-2d302a990978",
            
189
      "name": "computer vision",
230
      "name": "computer vision",
            
190
      "state": "active",
231
      "state": "active",
            
191
      "vocabulary_id": null
232
      "vocabulary_id": null
            
192
    },
233
    },
            
193
    {
234
    {
            
194
      "display_name": "point cloud registration",
235
      "display_name": "point cloud registration",
            
195
      "id": "d59935ec-0679-4b64-b68b-6aebadfa5845",
236
      "id": "d59935ec-0679-4b64-b68b-6aebadfa5845",
            
196
      "name": "point cloud registration",
237
      "name": "point cloud registration",
            
197
      "state": "active",
238
      "state": "active",
            
198
      "vocabulary_id": null
239
      "vocabulary_id": null
            
199
    }
240
    }
            
200
  ],
241
  ],
            
201
  "title": "ModelNet10",
242
  "title": "ModelNet10",
            
202
  "type": "dataset",
243
  "type": "dataset",
            
203
  "version": ""
244
  "version": ""
            
204
}
245
}