Changes
On December 2, 2024 at 10:08:29 PM UTC,
admin:
-
Changed title to Scannet (previously ScanNet)
-
Set author of Scannet to Kun Dai (previously Angela Dai)
-
Updated description of Scannet from
The ScanNet dataset contains 1.2K training samples and 312 validation samples annotated with per-point instances, semantic labels, and axis-aligned 3D bounding boxes belonging to 18 categories.
toThe dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.
-
Removed the following tags from Scannet
- indoor scenes
- scene understanding
- Instance Segmentation
- RGB-D Scans
- depth images
- RGBD
- 3D Data
- image matching
- 3D scene
- 3D object reconstruction
- Object Classification
- Robotics
- Point Clouds
- Augmented Reality
- 3D indoor scenes
- Multi-view
- monocular sequences
- object manipulation
- Indoor scene understanding
- Voxel-based Scenes
- object recognition
- Point Cloud Detection
- segmentation
- 3D Semantic Segmentation
- 3D Scans
- mobile manipulation
- Video Snippet
- poses
- computer vision
- RGB-D Images
- real scans
- Classification
- Autonomous Driving
- 3D point clouds
- stereo images
- 3D models
- large-scale dataset
- 3D object recognition
- point cloud data
- semantic mapping
- 3D Indoor Scene Understanding
- indoor scene understanding
- Indoor
- indoor
- semantic labels
- Indoor Scene Recognition
- 3D scene understanding
- Indoor Scene Reconstruction
- RGB-D Videos
- 3D
- scene segmentation
- 3D Models
- Camera Poses
- point cloud
- ScanNet
- Visual Grounding
- Semantic Segmentation
- 3D Scenes
- 3D Reconstructions
- Object Recognition
- 3D Object Detection
- 3D Point Clouds
- Computer Vision
- depth estimation
- 3D scene reconstruction
- understanding
- Scene Classification
- monocular vision
- 3D Indoor Scenes
- Indoor Scene Understanding
-
Added the following tags to Scannet
-
Changed value of field
defined_in
tohttps://doi.org/10.48550/arXiv.2207.07016
in Scannet -
Changed value of field
citation
to['https://doi.org/10.48550/arXiv.2103.12091', 'https://doi.org/10.48550/arXiv.2311.18610', 'https://doi.org/10.48550/arXiv.1908.03364']
in Scannet -
Changed value of field
extra_authors
to[{'extra_author': 'Tao Xie', 'orcid': ''}, {'extra_author': 'Ke Wang', 'orcid': ''}, {'extra_author': 'Zhiqiang Jiang', 'orcid': ''}, {'extra_author': 'Ruifeng Li', 'orcid': ''}, {'extra_author': 'Lijun Zhao', 'orcid': ''}]
in Scannet -
Deleted resource Original Metadata from Scannet
| f | 1 | { | f | 1 | { |
| 2 | "access_rights": "", | 2 | "access_rights": "", | ||
| n | 3 | "author": "Angela Dai", | n | 3 | "author": "Kun Dai", |
| 4 | "author_email": "", | 4 | "author_email": "", | ||
| 5 | "citation": [ | 5 | "citation": [ | ||
| n | 6 | "https://doi.org/10.48550/arXiv.2206.07710", | n | ||
| 7 | "https://doi.org/10.48550/arXiv.2004.04993", | ||||
| 8 | "https://doi.org/10.48550/arXiv.2305.13307", | ||||
| 9 | "https://doi.org/10.48550/arXiv.2310.01401", | ||||
| 10 | "https://doi.org/10.48550/arXiv.2308.07868", | ||||
| 11 | "https://doi.org/10.48550/arXiv.2306.05242", | ||||
| 12 | "https://doi.org/10.48550/arXiv.2407.10084", | ||||
| 13 | "https://doi.org/10.48550/arXiv.2312.03032", | ||||
| 14 | "https://doi.org/10.48550/arXiv.1908.06295", | ||||
| 15 | "https://doi.org/10.48550/arXiv.2404.06753", | ||||
| 16 | "https://doi.org/10.48550/arXiv.2203.01509", | ||||
| 17 | "https://doi.org/10.48550/arXiv.1901.09458", | ||||
| 18 | "https://doi.org/10.48550/arXiv.2105.06461", | ||||
| 19 | "https://doi.org/10.48550/arXiv.2405.17958", | ||||
| 20 | "https://doi.org/10.1145/3592131", | ||||
| 21 | "https://doi.org/10.48550/arXiv.2407.13642", | ||||
| 22 | "https://doi.org/10.48550/arXiv.1909.09287", | ||||
| 23 | "https://doi.org/10.48550/arXiv.2312.10390", | ||||
| 24 | "https://doi.org/10.48550/arXiv.2208.04309", | ||||
| 25 | "https://doi.org/10.48550/arXiv.2306.01923", | ||||
| 26 | "https://doi.org/10.48550/arXiv.2303.16646", | ||||
| 27 | "https://doi.org/10.48550/arXiv.2405.17176", | ||||
| 28 | "https://doi.org/10.48550/arXiv.2405.03221", | ||||
| 29 | "https://doi.org/10.48550/arXiv.2305.19201", | ||||
| 30 | "https://doi.org/10.48550/arXiv.2208.02879", | ||||
| 31 | "https://doi.org/10.48550/arXiv.2312.03026", | ||||
| 32 | "https://doi.org/10.48550/arXiv.2402.14464", | ||||
| 33 | "https://doi.org/10.48550/arXiv.1806.09573", | ||||
| 34 | "https://doi.org/10.48550/arXiv.2212.14197", | ||||
| 35 | "https://doi.org/10.48550/arXiv.1811.07246", | ||||
| 36 | "https://doi.org/10.48550/arXiv.2310.03420", | ||||
| 37 | "https://doi.org/10.48550/arXiv.2102.07373", | ||||
| 38 | "https://doi.org/10.48550/arXiv.2203.13318", | ||||
| 39 | "https://doi.org/10.48550/arXiv.2210.15107", | ||||
| 40 | "https://doi.org/10.48550/arXiv.2203.15245", | ||||
| 41 | "https://doi.org/10.48550/arXiv.2103.10339", | 6 | "https://doi.org/10.48550/arXiv.2103.12091", | ||
| 42 | "https://doi.org/10.48550/arXiv.2306.11051", | ||||
| 43 | "https://doi.org/10.48550/arXiv.2303.03361", | ||||
| 44 | "https://doi.org/10.48550/arXiv.1912.00145", | ||||
| 45 | "https://doi.org/10.48550/arXiv.2304.10179", | ||||
| 46 | "https://doi.org/10.48550/arXiv.2309.15702", | ||||
| 47 | "https://doi.org/10.1109/ICCVW.2019.00264", | ||||
| 48 | "https://doi.org/10.48550/arXiv.2403.07563", | ||||
| 49 | "https://doi.org/10.48550/arXiv.2311.12291", | 7 | "https://doi.org/10.48550/arXiv.2311.18610", | ||
| 50 | "https://doi.org/10.48550/arXiv.1906.08650" | 8 | "https://doi.org/10.48550/arXiv.1908.03364" | ||
| 51 | ], | 9 | ], | ||
| 52 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 10 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
| n | 53 | "defined_in": "https://doi.org/10.48550/arXiv.2405.12979", | n | 11 | "defined_in": "https://doi.org/10.48550/arXiv.2207.07016", |
| 54 | "doi": "10.57702/yv5yc1z0", | 12 | "doi": "10.57702/yv5yc1z0", | ||
| 55 | "doi_date_published": "2024-11-25", | 13 | "doi_date_published": "2024-11-25", | ||
| 56 | "doi_publisher": "TIB", | 14 | "doi_publisher": "TIB", | ||
| 57 | "doi_status": true, | 15 | "doi_status": true, | ||
| 58 | "domain": "https://service.tib.eu/ldmservice", | 16 | "domain": "https://service.tib.eu/ldmservice", | ||
| 59 | "extra_authors": [ | 17 | "extra_authors": [ | ||
| 60 | { | 18 | { | ||
| n | n | 19 | "extra_author": "Tao Xie", | ||
| 20 | "orcid": "" | ||||
| 21 | }, | ||||
| 22 | { | ||||
| 61 | "extra_author": "Angel X. Chang", | 23 | "extra_author": "Ke Wang", | ||
| 62 | "orcid": "" | 24 | "orcid": "" | ||
| 63 | }, | 25 | }, | ||
| 64 | { | 26 | { | ||
| n | 65 | "extra_author": "Manolis Savva", | n | 27 | "extra_author": "Zhiqiang Jiang", |
| 66 | "orcid": "" | 28 | "orcid": "" | ||
| 67 | }, | 29 | }, | ||
| 68 | { | 30 | { | ||
| n | 69 | "extra_author": "Maciej Halber", | n | 31 | "extra_author": "Ruifeng Li", |
| 70 | "orcid": "" | 32 | "orcid": "" | ||
| 71 | }, | 33 | }, | ||
| 72 | { | 34 | { | ||
| n | 73 | "extra_author": "Thomas Funkhouser", | n | 35 | "extra_author": "Lijun Zhao", |
| 74 | "orcid": "" | ||||
| 75 | }, | ||||
| 76 | { | ||||
| 77 | "extra_author": "Matthias Nie\u00dfner", | ||||
| 78 | "orcid": "" | 36 | "orcid": "" | ||
| 79 | } | 37 | } | ||
| 80 | ], | 38 | ], | ||
| 81 | "groups": [ | 39 | "groups": [ | ||
| n | 82 | { | n | ||
| 83 | "description": "", | ||||
| 84 | "display_name": "3D Data", | ||||
| 85 | "id": "75c2b2af-ff6b-4c10-9678-a19d3b6941ee", | ||||
| 86 | "image_display_url": "", | ||||
| 87 | "name": "3d-data", | ||||
| 88 | "title": "3D Data" | ||||
| 89 | }, | ||||
| 90 | { | ||||
| 91 | "description": "", | ||||
| 92 | "display_name": "3D Indoor Scene Understanding", | ||||
| 93 | "id": "1277aa66-82c9-4b25-afef-a65971caacd0", | ||||
| 94 | "image_display_url": "", | ||||
| 95 | "name": "3d-indoor-scene-understanding", | ||||
| 96 | "title": "3D Indoor Scene Understanding" | ||||
| 97 | }, | ||||
| 98 | { | ||||
| 99 | "description": "", | ||||
| 100 | "display_name": "3D Indoor Scenes", | ||||
| 101 | "id": "d412ff56-9507-4707-980b-d70904021eea", | ||||
| 102 | "image_display_url": "", | ||||
| 103 | "name": "3d-indoor-scenes", | ||||
| 104 | "title": "3D Indoor Scenes" | ||||
| 105 | }, | ||||
| 106 | { | ||||
| 107 | "description": "", | ||||
| 108 | "display_name": "3D Instance Segmentation", | ||||
| 109 | "id": "df7e5dbf-bb57-4ebd-a62f-f2a1b16d3ed3", | ||||
| 110 | "image_display_url": "", | ||||
| 111 | "name": "3d-instance-segmentation", | ||||
| 112 | "title": "3D Instance Segmentation" | ||||
| 113 | }, | ||||
| 114 | { | ||||
| 115 | "description": "", | ||||
| 116 | "display_name": "3D Object Detection", | ||||
| 117 | "id": "b9c5385e-2cc8-4698-95f2-a1528ebb0182", | ||||
| 118 | "image_display_url": "", | ||||
| 119 | "name": "3d-object-detection", | ||||
| 120 | "title": "3D Object Detection" | ||||
| 121 | }, | ||||
| 122 | { | ||||
| 123 | "description": "", | ||||
| 124 | "display_name": "3D Object Recognition", | ||||
| 125 | "id": "fb966b89-2ab8-4ac3-ae8e-55e18c4de9e1", | ||||
| 126 | "image_display_url": "", | ||||
| 127 | "name": "3d-object-recognition", | ||||
| 128 | "title": "3D Object Recognition" | ||||
| 129 | }, | ||||
| 130 | { | ||||
| 131 | "description": "", | ||||
| 132 | "display_name": "3D Object Reconstruction", | ||||
| 133 | "id": "1501f86f-3b7f-42bd-a0f3-f53ac1414787", | ||||
| 134 | "image_display_url": "", | ||||
| 135 | "name": "3d-object-reconstruction", | ||||
| 136 | "title": "3D Object Reconstruction" | ||||
| 137 | }, | ||||
| 138 | { | ||||
| 139 | "description": "", | ||||
| 140 | "display_name": "3D Point Clouds", | ||||
| 141 | "id": "625c463b-8cb0-4180-af63-ed8448fb60dd", | ||||
| 142 | "image_display_url": "", | ||||
| 143 | "name": "3d-point-clouds", | ||||
| 144 | "title": "3D Point Clouds" | ||||
| 145 | }, | ||||
| 146 | { | 40 | { | ||
| 147 | "description": "", | 41 | "description": "", | ||
| 148 | "display_name": "3D Reconstruction", | 42 | "display_name": "3D Reconstruction", | ||
| 149 | "id": "4f8c7487-ad74-48b5-af62-efc1369527f9", | 43 | "id": "4f8c7487-ad74-48b5-af62-efc1369527f9", | ||
| 150 | "image_display_url": "", | 44 | "image_display_url": "", | ||
| 151 | "name": "3d-reconstruction", | 45 | "name": "3d-reconstruction", | ||
| 152 | "title": "3D Reconstruction" | 46 | "title": "3D Reconstruction" | ||
| 153 | }, | 47 | }, | ||
| 154 | { | 48 | { | ||
| 155 | "description": "", | 49 | "description": "", | ||
| n | 156 | "display_name": "3D Scene Reconstruction", | n | ||
| 157 | "id": "b4a67da3-165a-4e82-a50e-49bccd9d92f9", | ||||
| 158 | "image_display_url": "", | ||||
| 159 | "name": "3d-scene-reconstruction", | ||||
| 160 | "title": "3D Scene Reconstruction" | ||||
| 161 | }, | ||||
| 162 | { | ||||
| 163 | "description": "", | ||||
| 164 | "display_name": "3D Scene Understanding", | 50 | "display_name": "3D Scene Understanding", | ||
| 165 | "id": "bbef9259-bb19-4e31-8610-6745016a53bc", | 51 | "id": "bbef9259-bb19-4e31-8610-6745016a53bc", | ||
| 166 | "image_display_url": "", | 52 | "image_display_url": "", | ||
| 167 | "name": "3d-scene-understanding", | 53 | "name": "3d-scene-understanding", | ||
| 168 | "title": "3D Scene Understanding" | 54 | "title": "3D Scene Understanding" | ||
| 169 | }, | 55 | }, | ||
| 170 | { | 56 | { | ||
| 171 | "description": "", | 57 | "description": "", | ||
| n | 172 | "display_name": "3D Scenes", | n | ||
| 173 | "id": "2ffc9813-4a9c-4253-8e68-07ea417c2314", | ||||
| 174 | "image_display_url": "", | ||||
| 175 | "name": "3d-scenes", | ||||
| 176 | "title": "3D Scenes" | ||||
| 177 | }, | ||||
| 178 | { | ||||
| 179 | "description": "", | ||||
| 180 | "display_name": "3D Semantic Segmentation", | ||||
| 181 | "id": "4b4f04aa-d4f5-497c-87dc-f8c937d3e844", | ||||
| 182 | "image_display_url": "", | ||||
| 183 | "name": "3d-semantic-segmentation", | ||||
| 184 | "title": "3D Semantic Segmentation" | ||||
| 185 | }, | ||||
| 186 | { | ||||
| 187 | "description": "", | ||||
| 188 | "display_name": "3D Semantic Understanding", | ||||
| 189 | "id": "f9240e94-b5df-4ebd-b416-177741f8d89f", | ||||
| 190 | "image_display_url": "", | ||||
| 191 | "name": "3d-semantic-understanding", | ||||
| 192 | "title": "3D Semantic Understanding" | ||||
| 193 | }, | ||||
| 194 | { | ||||
| 195 | "description": "", | ||||
| 196 | "display_name": "Classification", | ||||
| 197 | "id": "57bb78f3-a48d-4716-a9cc-0028df74b1c5", | ||||
| 198 | "image_display_url": "", | ||||
| 199 | "name": "classification", | ||||
| 200 | "title": "Classification" | ||||
| 201 | }, | ||||
| 202 | { | ||||
| 203 | "description": "", | ||||
| 204 | "display_name": "Computer Vision", | 58 | "display_name": "Computer Vision", | ||
| 205 | "id": "d09caf7c-26c7-4e4d-bb8e-49476a90ba25", | 59 | "id": "d09caf7c-26c7-4e4d-bb8e-49476a90ba25", | ||
| 206 | "image_display_url": "", | 60 | "image_display_url": "", | ||
| 207 | "name": "computer-vision", | 61 | "name": "computer-vision", | ||
| 208 | "title": "Computer Vision" | 62 | "title": "Computer Vision" | ||
| 209 | }, | 63 | }, | ||
| 210 | { | 64 | { | ||
| 211 | "description": "", | 65 | "description": "", | ||
| n | 212 | "display_name": "Depth Estimation", | n | ||
| 213 | "id": "1cfc3f7a-9b2f-4ee9-9d15-9883618b3218", | ||||
| 214 | "image_display_url": "", | ||||
| 215 | "name": "depth-estimation", | ||||
| 216 | "title": "Depth Estimation" | ||||
| 217 | }, | ||||
| 218 | { | ||||
| 219 | "description": "", | ||||
| 220 | "display_name": "Indoor Scene Recognition", | ||||
| 221 | "id": "d8b3b22e-519e-44c7-95d4-e5cc5bdd7dc3", | ||||
| 222 | "image_display_url": "", | ||||
| 223 | "name": "indoor-scene-recognition", | ||||
| 224 | "title": "Indoor Scene Recognition" | ||||
| 225 | }, | ||||
| 226 | { | ||||
| 227 | "description": "", | ||||
| 228 | "display_name": "Indoor Scene Reconstruction", | 66 | "display_name": "Indoor Scene Reconstruction", | ||
| 229 | "id": "91936444-bc66-44e6-9a35-790c6130c384", | 67 | "id": "91936444-bc66-44e6-9a35-790c6130c384", | ||
| 230 | "image_display_url": "", | 68 | "image_display_url": "", | ||
| 231 | "name": "indoor-scene-reconstruction", | 69 | "name": "indoor-scene-reconstruction", | ||
| 232 | "title": "Indoor Scene Reconstruction" | 70 | "title": "Indoor Scene Reconstruction" | ||
| 233 | }, | 71 | }, | ||
| 234 | { | 72 | { | ||
| 235 | "description": "", | 73 | "description": "", | ||
| n | 236 | "display_name": "Indoor Scene Understanding", | n | ||
| 237 | "id": "6f671b4b-0b4d-4fe1-9769-e31ab164e205", | ||||
| 238 | "image_display_url": "", | ||||
| 239 | "name": "indoor-scene-understanding", | ||||
| 240 | "title": "Indoor Scene Understanding" | ||||
| 241 | }, | ||||
| 242 | { | ||||
| 243 | "description": "", | ||||
| 244 | "display_name": "Indoor Scenes", | 74 | "display_name": "Indoor Scenes", | ||
| 245 | "id": "1cdde29d-6358-4f23-823c-0e7caad9ae2e", | 75 | "id": "1cdde29d-6358-4f23-823c-0e7caad9ae2e", | ||
| 246 | "image_display_url": "", | 76 | "image_display_url": "", | ||
| 247 | "name": "indoor-scenes", | 77 | "name": "indoor-scenes", | ||
| 248 | "title": "Indoor Scenes" | 78 | "title": "Indoor Scenes" | ||
| 249 | }, | 79 | }, | ||
| 250 | { | 80 | { | ||
| 251 | "description": "", | 81 | "description": "", | ||
| n | 252 | "display_name": "Monocular Sequences", | n | ||
| 253 | "id": "0ec523a9-3a74-41ce-9cb0-8f35ab9f780d", | ||||
| 254 | "image_display_url": "", | ||||
| 255 | "name": "monocular-sequences", | ||||
| 256 | "title": "Monocular Sequences" | ||||
| 257 | }, | ||||
| 258 | { | ||||
| 259 | "description": "", | ||||
| 260 | "display_name": "Monocular Vision", | ||||
| 261 | "id": "9cf940eb-e2c0-4ddd-a103-3e91c84be459", | ||||
| 262 | "image_display_url": "", | ||||
| 263 | "name": "monocular-vision", | ||||
| 264 | "title": "Monocular Vision" | ||||
| 265 | }, | ||||
| 266 | { | ||||
| 267 | "description": "", | ||||
| 268 | "display_name": "Object Detection", | 82 | "display_name": "Object Detection", | ||
| 269 | "id": "ca2cb1af-d31c-49b0-a1dd-62b22f2b9e20", | 83 | "id": "ca2cb1af-d31c-49b0-a1dd-62b22f2b9e20", | ||
| 270 | "image_display_url": "", | 84 | "image_display_url": "", | ||
| 271 | "name": "object-detection", | 85 | "name": "object-detection", | ||
| 272 | "title": "Object Detection" | 86 | "title": "Object Detection" | ||
| 273 | }, | 87 | }, | ||
| 274 | { | 88 | { | ||
| 275 | "description": "", | 89 | "description": "", | ||
| n | 276 | "display_name": "Object Manipulation", | n | ||
| 277 | "id": "e8df4193-2441-4a78-9510-6040d9962e08", | ||||
| 278 | "image_display_url": "", | ||||
| 279 | "name": "object-manipulation", | ||||
| 280 | "title": "Object Manipulation" | ||||
| 281 | }, | ||||
| 282 | { | ||||
| 283 | "description": "", | ||||
| 284 | "display_name": "Object Recognition", | ||||
| 285 | "id": "1b471529-d821-46c3-8ac0-1ec99f0c80bc", | ||||
| 286 | "image_display_url": "", | ||||
| 287 | "name": "object-recognition", | ||||
| 288 | "title": "Object Recognition" | ||||
| 289 | }, | ||||
| 290 | { | ||||
| 291 | "description": "", | ||||
| 292 | "display_name": "RGB-D Videos", | ||||
| 293 | "id": "847ae5dc-0c8c-4d84-9632-22cc7ba0920c", | ||||
| 294 | "image_display_url": "", | ||||
| 295 | "name": "rgb-d-videos", | ||||
| 296 | "title": "RGB-D Videos" | ||||
| 297 | }, | ||||
| 298 | { | ||||
| 299 | "description": "", | ||||
| 300 | "display_name": "Scene Analysis", | ||||
| 301 | "id": "09b016b4-749a-4dc8-9af5-ef7f0035a0ef", | ||||
| 302 | "image_display_url": "", | ||||
| 303 | "name": "scene-analysis", | ||||
| 304 | "title": "Scene Analysis" | ||||
| 305 | }, | ||||
| 306 | { | ||||
| 307 | "description": "", | ||||
| 308 | "display_name": "Scene Segmentation", | ||||
| 309 | "id": "69266901-10c5-4367-b59e-e438f0dd6e34", | ||||
| 310 | "image_display_url": "", | ||||
| 311 | "name": "scene-segmentation", | ||||
| 312 | "title": "Scene Segmentation" | ||||
| 313 | }, | ||||
| 314 | { | ||||
| 315 | "description": "", | ||||
| 316 | "display_name": "Scene Understanding", | 90 | "display_name": "Scene Understanding", | ||
| 317 | "id": "f3a9e19d-4b53-4b7d-bd4b-65c69132188a", | 91 | "id": "f3a9e19d-4b53-4b7d-bd4b-65c69132188a", | ||
| 318 | "image_display_url": "", | 92 | "image_display_url": "", | ||
| 319 | "name": "scene-understanding", | 93 | "name": "scene-understanding", | ||
| 320 | "title": "Scene Understanding" | 94 | "title": "Scene Understanding" | ||
| n | 321 | }, | n | ||
| 322 | { | ||||
| 323 | "description": "", | ||||
| 324 | "display_name": "Semantic Segmentation", | ||||
| 325 | "id": "8c3f2eee-f5f9-464d-9c0a-1a5e7a925c0e", | ||||
| 326 | "image_display_url": "", | ||||
| 327 | "name": "semantic-segmentation", | ||||
| 328 | "title": "Semantic Segmentation" | ||||
| 329 | } | 95 | } | ||
| 330 | ], | 96 | ], | ||
| 331 | "id": "239f4ce8-1dc9-4466-9fb6-fbb172bfb839", | 97 | "id": "239f4ce8-1dc9-4466-9fb6-fbb172bfb839", | ||
| 332 | "isopen": false, | 98 | "isopen": false, | ||
| 333 | "landing_page": "https://www.scannet.org/", | 99 | "landing_page": "https://www.scannet.org/", | ||
| 334 | "license_title": null, | 100 | "license_title": null, | ||
| 335 | "link_orkg": "", | 101 | "link_orkg": "", | ||
| 336 | "metadata_created": "2024-11-25T15:03:48.405672", | 102 | "metadata_created": "2024-11-25T15:03:48.405672", | ||
| n | 337 | "metadata_modified": "2024-12-02T17:50:24.818081", | n | 103 | "metadata_modified": "2024-12-02T22:08:28.312594", |
| 338 | "name": "scannet", | 104 | "name": "scannet", | ||
| n | 339 | "notes": "The ScanNet dataset contains 1.2K training samples and 312 | n | 105 | "notes": "The dataset used for training and testing the proposed |
| 340 | validation samples annotated with per-point instances, semantic | 106 | RGBD-based obstacle avoidance system for visually impaired people.", | ||
| 341 | labels, and axis-aligned 3D bounding boxes belonging to 18 | ||||
| 342 | categories.", | ||||
| 343 | "num_resources": 1, | 107 | "num_resources": 0, | ||
| 344 | "num_tags": 79, | 108 | "num_tags": 13, | ||
| 345 | "organization": { | 109 | "organization": { | ||
| 346 | "approval_status": "approved", | 110 | "approval_status": "approved", | ||
| 347 | "created": "2024-11-25T12:11:38.292601", | 111 | "created": "2024-11-25T12:11:38.292601", | ||
| 348 | "description": "", | 112 | "description": "", | ||
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| 350 | "image_url": "", | 114 | "image_url": "", | ||
| 351 | "is_organization": true, | 115 | "is_organization": true, | ||
| 352 | "name": "no-organization", | 116 | "name": "no-organization", | ||
| 353 | "state": "active", | 117 | "state": "active", | ||
| 354 | "title": "No Organization", | 118 | "title": "No Organization", | ||
| 355 | "type": "organization" | 119 | "type": "organization" | ||
| 356 | }, | 120 | }, | ||
| 357 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 121 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
| 358 | "private": false, | 122 | "private": false, | ||
| 359 | "relationships_as_object": [], | 123 | "relationships_as_object": [], | ||
| 360 | "relationships_as_subject": [], | 124 | "relationships_as_subject": [], | ||
| n | 361 | "resources": [ | n | 125 | "resources": [], |
| 362 | { | ||||
| 363 | "cache_last_updated": null, | ||||
| 364 | "cache_url": null, | ||||
| 365 | "created": "2024-12-02T18:38:42", | ||||
| 366 | "data": [ | ||||
| 367 | "dcterms:title", | ||||
| 368 | "dcterms:accessRights", | ||||
| 369 | "dcterms:creator", | ||||
| 370 | "dcterms:description", | ||||
| 371 | "dcterms:issued", | ||||
| 372 | "dcterms:language", | ||||
| 373 | "dcterms:identifier", | ||||
| 374 | "dcat:theme", | ||||
| 375 | "dcterms:type", | ||||
| 376 | "dcat:keyword", | ||||
| 377 | "dcat:landingPage", | ||||
| 378 | "dcterms:hasVersion", | ||||
| 379 | "dcterms:format", | ||||
| 380 | "mls:task", | ||||
| 381 | "datacite:isDescribedBy" | ||||
| 382 | ], | ||||
| 383 | "description": "The json representation of the dataset with its | ||||
| 384 | distributions based on DCAT.", | ||||
| 385 | "format": "JSON", | ||||
| 386 | "hash": "", | ||||
| 387 | "id": "f05f22e6-7825-41e8-8e9d-b454bcbd0514", | ||||
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| 390 | "mimetype": "application/json", | ||||
| 391 | "mimetype_inner": null, | ||||
| 392 | "name": "Original Metadata", | ||||
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| 399 | resource/f05f22e6-7825-41e8-8e9d-b454bcbd0514/download/metadata.json", | ||||
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| 403 | "services_used_list": "", | 126 | "services_used_list": "", | ||
| 404 | "state": "active", | 127 | "state": "active", | ||
| 405 | "tags": [ | 128 | "tags": [ | ||
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| 462 | { | 136 | { | ||
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| 481 | "vocabulary_id": null | 141 | "vocabulary_id": null | ||
| 482 | }, | 142 | }, | ||
| 483 | { | 143 | { | ||
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| 486 | "name": "3D Scenes", | ||||
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| 490 | { | ||||
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| 493 | "name": "3D Semantic Segmentation", | ||||
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| 500 | "name": "3D indoor scenes", | ||||
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| 511 | { | ||||
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| 514 | "name": "3D object recognition", | ||||
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| 525 | { | ||||
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| 528 | "name": "3D point clouds", | ||||
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| 539 | { | 150 | { | ||
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| 546 | { | ||||
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| 560 | { | ||||
| 561 | "display_name": "Augmented Reality", | ||||
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| 577 | "name": "Camera Poses", | ||||
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| 693 | { | 171 | { | ||
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| 738 | "name": "ScanNet", | 181 | "name": "Scannet", | ||
| 739 | "state": "active", | 182 | "state": "active", | ||
| 740 | "vocabulary_id": null | 183 | "vocabulary_id": null | ||
| 741 | }, | 184 | }, | ||
| 742 | { | 185 | { | ||
| n | 743 | "display_name": "Scene Classification", | n | 186 | "display_name": "Scans", |
| 744 | "id": "469ec5ef-5ed9-4ddc-8eba-9d8eba833076", | 187 | "id": "67f2cb5a-d434-4da9-a96b-85f08b51685e", | ||
| 745 | "name": "Scene Classification", | 188 | "name": "Scans", | ||
| 746 | "state": "active", | 189 | "state": "active", | ||
| 747 | "vocabulary_id": null | 190 | "vocabulary_id": null | ||
| 748 | }, | 191 | }, | ||
| 749 | { | 192 | { | ||
| 750 | "display_name": "Scene Understanding", | 193 | "display_name": "Scene Understanding", | ||
| 751 | "id": "d066cee5-d8a2-4d28-ba93-a32fa6a30b26", | 194 | "id": "d066cee5-d8a2-4d28-ba93-a32fa6a30b26", | ||
| 752 | "name": "Scene Understanding", | 195 | "name": "Scene Understanding", | ||
| 753 | "state": "active", | 196 | "state": "active", | ||
| 754 | "vocabulary_id": null | 197 | "vocabulary_id": null | ||
| 755 | }, | 198 | }, | ||
| 756 | { | 199 | { | ||
| n | 757 | "display_name": "Semantic Segmentation", | n | ||
| 758 | "id": "809ad6af-28cd-43bd-974d-055a5c0f2973", | ||||
| 759 | "name": "Semantic Segmentation", | ||||
| 760 | "state": "active", | ||||
| 761 | "vocabulary_id": null | ||||
| 762 | }, | ||||
| 763 | { | ||||
| 764 | "display_name": "Video Snippet", | ||||
| 765 | "id": "d674eac3-3597-461e-bdb8-87c0571fe4b0", | ||||
| 766 | "name": "Video Snippet", | ||||
| 767 | "state": "active", | ||||
| 768 | "vocabulary_id": null | ||||
| 769 | }, | ||||
| 770 | { | ||||
| 771 | "display_name": "Visual Grounding", | ||||
| 772 | "id": "82dad3b4-e1c2-47b9-9aa8-dc78c0274934", | ||||
| 773 | "name": "Visual Grounding", | ||||
| 774 | "state": "active", | ||||
| 775 | "vocabulary_id": null | ||||
| 776 | }, | ||||
| 777 | { | ||||
| 778 | "display_name": "Voxel-based Scenes", | ||||
| 779 | "id": "3435b04c-a8ae-48a3-8dee-aab7b9ef7025", | ||||
| 780 | "name": "Voxel-based Scenes", | ||||
| 781 | "state": "active", | ||||
| 782 | "vocabulary_id": null | ||||
| 783 | }, | ||||
| 784 | { | ||||
| 785 | "display_name": "computer vision", | ||||
| 786 | "id": "f650b4e3-9955-49b0-ba7b-2d302a990978", | ||||
| 787 | "name": "computer vision", | ||||
| 788 | "state": "active", | ||||
| 789 | "vocabulary_id": null | ||||
| 790 | }, | ||||
| 791 | { | ||||
| 792 | "display_name": "depth estimation", | ||||
| 793 | "id": "3c08a798-cec3-4682-a668-4f95d6d8ad18", | ||||
| 794 | "name": "depth estimation", | ||||
| 795 | "state": "active", | ||||
| 796 | "vocabulary_id": null | ||||
| 797 | }, | ||||
| 798 | { | ||||
| 799 | "display_name": "depth images", | 200 | "display_name": "ground-truth poses", | ||
| 800 | "id": "589c28de-8401-4803-8cb6-b46c39d73dcc", | 201 | "id": "d38024cd-d9b8-41d6-ba2f-3a0155fb25bb", | ||
| 801 | "name": "depth images", | 202 | "name": "ground-truth poses", | ||
| 802 | "state": "active", | ||||
| 803 | "vocabulary_id": null | ||||
| 804 | }, | ||||
| 805 | { | ||||
| 806 | "display_name": "image matching", | ||||
| 807 | "id": "941d5b3d-48d3-44c9-9529-7666b0aec81c", | ||||
| 808 | "name": "image matching", | ||||
| 809 | "state": "active", | ||||
| 810 | "vocabulary_id": null | ||||
| 811 | }, | ||||
| 812 | { | ||||
| 813 | "display_name": "indoor", | ||||
| 814 | "id": "6e9bd880-03e3-4012-b961-78941f1435c0", | ||||
| 815 | "name": "indoor", | ||||
| 816 | "state": "active", | 203 | "state": "active", | ||
| 817 | "vocabulary_id": null | 204 | "vocabulary_id": null | ||
| 818 | }, | 205 | }, | ||
| 819 | { | 206 | { | ||
| 820 | "display_name": "indoor scene", | 207 | "display_name": "indoor scene", | ||
| 821 | "id": "6ea936b7-33d6-416a-9639-c65133309404", | 208 | "id": "6ea936b7-33d6-416a-9639-c65133309404", | ||
| 822 | "name": "indoor scene", | 209 | "name": "indoor scene", | ||
| 823 | "state": "active", | 210 | "state": "active", | ||
| 824 | "vocabulary_id": null | 211 | "vocabulary_id": null | ||
| 825 | }, | 212 | }, | ||
| 826 | { | 213 | { | ||
| n | 827 | "display_name": "indoor scene understanding", | n | ||
| 828 | "id": "09081b59-cdf6-41ee-9ce4-939ea1045586", | ||||
| 829 | "name": "indoor scene understanding", | ||||
| 830 | "state": "active", | ||||
| 831 | "vocabulary_id": null | ||||
| 832 | }, | ||||
| 833 | { | ||||
| 834 | "display_name": "indoor scenes", | ||||
| 835 | "id": "7c39b0f6-b707-459a-9d7e-7e2625177b9f", | ||||
| 836 | "name": "indoor scenes", | ||||
| 837 | "state": "active", | ||||
| 838 | "vocabulary_id": null | ||||
| 839 | }, | ||||
| 840 | { | ||||
| 841 | "display_name": "large-scale dataset", | ||||
| 842 | "id": "a9c694bf-f591-4625-a20e-d53d3f90d489", | ||||
| 843 | "name": "large-scale dataset", | ||||
| 844 | "state": "active", | ||||
| 845 | "vocabulary_id": null | ||||
| 846 | }, | ||||
| 847 | { | ||||
| 848 | "display_name": "mobile manipulation", | ||||
| 849 | "id": "f06b740c-425b-4ccd-b230-46bf5490390e", | ||||
| 850 | "name": "mobile manipulation", | ||||
| 851 | "state": "active", | ||||
| 852 | "vocabulary_id": null | ||||
| 853 | }, | ||||
| 854 | { | ||||
| 855 | "display_name": "monocular sequences", | ||||
| 856 | "id": "7d8f73f2-4a98-4965-b2fc-132121782a61", | ||||
| 857 | "name": "monocular sequences", | ||||
| 858 | "state": "active", | ||||
| 859 | "vocabulary_id": null | ||||
| 860 | }, | ||||
| 861 | { | ||||
| 862 | "display_name": "monocular vision", | ||||
| 863 | "id": "4a1b42c2-bc18-4b0f-bbf3-5c453bebed3b", | ||||
| 864 | "name": "monocular vision", | ||||
| 865 | "state": "active", | ||||
| 866 | "vocabulary_id": null | ||||
| 867 | }, | ||||
| 868 | { | ||||
| 869 | "display_name": "object manipulation", | ||||
| 870 | "id": "7982e325-2a38-4ce1-b8e7-7ca43a0376f4", | ||||
| 871 | "name": "object manipulation", | ||||
| 872 | "state": "active", | ||||
| 873 | "vocabulary_id": null | ||||
| 874 | }, | ||||
| 875 | { | ||||
| 876 | "display_name": "object recognition", | ||||
| 877 | "id": "cbac3f3d-b235-44b0-8bfb-30bf00e44b8c", | ||||
| 878 | "name": "object recognition", | ||||
| 879 | "state": "active", | ||||
| 880 | "vocabulary_id": null | ||||
| 881 | }, | ||||
| 882 | { | ||||
| 883 | "display_name": "point cloud", | ||||
| 884 | "id": "fcb968fe-38b8-4b04-a049-e69bcf7b9f19", | ||||
| 885 | "name": "point cloud", | ||||
| 886 | "state": "active", | ||||
| 887 | "vocabulary_id": null | ||||
| 888 | }, | ||||
| 889 | { | ||||
| 890 | "display_name": "point cloud data", | ||||
| 891 | "id": "2072985c-8377-4acd-9d25-57a4830b9664", | ||||
| 892 | "name": "point cloud data", | ||||
| 893 | "state": "active", | ||||
| 894 | "vocabulary_id": null | ||||
| 895 | }, | ||||
| 896 | { | ||||
| 897 | "display_name": "poses", | ||||
| 898 | "id": "f2595e7c-4791-42fe-b42b-273552c610ee", | ||||
| 899 | "name": "poses", | ||||
| 900 | "state": "active", | ||||
| 901 | "vocabulary_id": null | ||||
| 902 | }, | ||||
| 903 | { | ||||
| 904 | "display_name": "real scans", | ||||
| 905 | "id": "d9d5381e-dde0-4938-8b1e-cad20c8178e6", | ||||
| 906 | "name": "real scans", | ||||
| 907 | "state": "active", | ||||
| 908 | "vocabulary_id": null | ||||
| 909 | }, | ||||
| 910 | { | ||||
| 911 | "display_name": "scene segmentation", | ||||
| 912 | "id": "94db0e66-2f29-4c88-bc8d-63001457117a", | ||||
| 913 | "name": "scene segmentation", | ||||
| 914 | "state": "active", | ||||
| 915 | "vocabulary_id": null | ||||
| 916 | }, | ||||
| 917 | { | ||||
| 918 | "display_name": "scene understanding", | ||||
| 919 | "id": "d56fbd48-e5ed-410d-8c4f-e698955e28fa", | ||||
| 920 | "name": "scene understanding", | ||||
| 921 | "state": "active", | ||||
| 922 | "vocabulary_id": null | ||||
| 923 | }, | ||||
| 924 | { | ||||
| 925 | "display_name": "segmentation", | ||||
| 926 | "id": "7ce0e509-9f57-44c4-a015-f1ab9872bb44", | ||||
| 927 | "name": "segmentation", | ||||
| 928 | "state": "active", | ||||
| 929 | "vocabulary_id": null | ||||
| 930 | }, | ||||
| 931 | { | ||||
| 932 | "display_name": "semantic labels", | ||||
| 933 | "id": "bb5e3c7e-238d-4740-9b9b-adcfee58feea", | ||||
| 934 | "name": "semantic labels", | ||||
| 935 | "state": "active", | ||||
| 936 | "vocabulary_id": null | ||||
| 937 | }, | ||||
| 938 | { | ||||
| 939 | "display_name": "semantic mapping", | ||||
| 940 | "id": "75ba1d45-c664-43c6-8a36-bd2e8ed944b2", | ||||
| 941 | "name": "semantic mapping", | ||||
| 942 | "state": "active", | ||||
| 943 | "vocabulary_id": null | ||||
| 944 | }, | ||||
| 945 | { | ||||
| 946 | "display_name": "stereo images", | ||||
| 947 | "id": "710f3486-4ead-4428-97d9-d3fa8f90bea4", | ||||
| 948 | "name": "stereo images", | ||||
| 949 | "state": "active", | ||||
| 950 | "vocabulary_id": null | ||||
| 951 | }, | ||||
| 952 | { | ||||
| 953 | "display_name": "understanding", | 214 | "display_name": "scanning", | ||
| 954 | "id": "08af4a45-f67e-4d3a-a8d5-c03e8bb54291", | 215 | "id": "13b6e2c4-edcb-4e86-92d0-499e934ca685", | ||
| 955 | "name": "understanding", | 216 | "name": "scanning", | ||
| 956 | "state": "active", | 217 | "state": "active", | ||
| 957 | "vocabulary_id": null | 218 | "vocabulary_id": null | ||
| 958 | } | 219 | } | ||
| 959 | ], | 220 | ], | ||
| t | 960 | "title": "ScanNet", | t | 221 | "title": "Scannet", |
| 961 | "type": "dataset", | 222 | "type": "dataset", | ||
| 962 | "version": "" | 223 | "version": "" | ||
| 963 | } | 224 | } |