Add metadata and paper/code links

README.md

@@ -1,222 +1,231 @@
-# GeoGR-67K-CAT
-
-This repository contains the data used in **GeoGR: Spatial–Semantic Token Compression and Group Routing for Efficient 3D VLMs**.
-
-The release includes ScanNet scene data and derived 3D representations, EmbodiedScan scene metadata, geometry and frame-selection metadata, benchmark annotations for five downstream 3D-language tasks, and the processed training data used in our experiments.
-
-The scene representation and data preprocessing follow the general pipeline of [Video-3D-LLM](https://github.com/LaVi-Lab/Video-3D-LLM).
-
-## Dataset Organization
-
-After extracting the ScanNet pose data, the expected directory structure is:
-
-```
-GeoGR-67K-CAT/
-├── scannet/
-│   ├── scans/
-│   ├── pose/
-│   ├── pcd_with_object_aabbs/
-│   └── mask/
-│
-├── embodiedscan/
-│   ├── embodiedscan_infos_train.pkl
-│   ├── embodiedscan_infos_val.pkl
-│   └── embodiedscan_infos_test.pkl
-│
-├── metadata/
-│   ├── scannet_select_frames.json
-│   ├── pcd_discrete_0.1.pkl
-│   ├── scannet_train_gt_box.json
-│   ├── scannet_val_gt_box.json
-│   ├── scannet_val_pred_box.json
-│   └── scannet_test_pred_box.json
-│
-├── benchmark/
-│   ├── Sqa3D/
-│   ├── ScanQA/
-│   ├── ScanRefer/
-│   ├── Scan2Cap/
-│   └── Multi3DRefer/
-│
-└── processed/
-    ├── full/
-    └── subsets/
-```
-
-## ScanNet Data
-
-The `scannet/` directory contains the ScanNet scene data and derived scene representations used in GeoGR.
-
-### Scans
-
-`scannet/scans/` contains the original ScanNet v2 scene files. Each scene follows the standard ScanNet organization:
-
-```
-scannet/
-└── scans/
-    └── sceneXXXX_XX/
-        ├── sceneXXXX_XX_vh_clean_2.ply
-        ├── sceneXXXX_XX_vh_clean_2.0.010000.segs.json
-        ├── sceneXXXX_XX.aggregation.json
-        ├── sceneXXXX_XX.txt
-        └── sceneXXXX_XX.sens
-```
-
-The original ScanNet v2 data can be obtained from the [official ScanNet website](http://www.scan-net.org/).
-
-### Pose
-
-`scannet/pose/` contains the preprocessed multi-view observations for each scene, including RGB images, depth information, and camera poses.
-
-The pose data are distributed as five split archive files (`posed_images_part_aa` to `posed_images_part_ae`). After downloading, merge and extract them into `scannet/pose/` with:
-
-```
-cd scannet && cat posed_images_part_a{a,b,c,d,e} > posed_images.tar.gz && mkdir -p pose && tar -xzf posed_images.tar.gz -C pose && rm posed_images.tar.gz
-```
-
-### Point Clouds and Object Boxes
-
-`scannet/pcd_with_object_aabbs/` contains processed scene point clouds together with object-level axis-aligned 3D bounding boxes.
-
-### Object Masks
-
-`scannet/mask/` contains the object-mask and proposal information used by the scene processing and evaluation pipeline.
-
-The corresponding ScanNet preprocessing scripts are available in the [Video-3D-LLM 3D preprocessing directory](https://github.com/LaVi-Lab/Video-3D-LLM/tree/main/scripts/3d/preprocessing).
-
-## EmbodiedScan Metadata
-
-The `embodiedscan/` directory contains the scene-information files used to associate multi-view observations with their corresponding 3D scenes.
-
-```
-embodiedscan/
-├── embodiedscan_infos_train.pkl
-├── embodiedscan_infos_val.pkl
-└── embodiedscan_infos_test.pkl
-```
-
-The original EmbodiedScan data can be obtained from the [official EmbodiedScan repository](https://github.com/OpenRobotLab/EmbodiedScan/tree/main/data). Access may require completing the corresponding data request procedure.
-
-## Metadata
-
-The `metadata/` directory contains auxiliary geometry, frame-selection, and object-box information used by the GeoGR data pipeline.
-
-```
-metadata/
-├── scannet_select_frames.json
-├── pcd_discrete_0.1.pkl
-├── scannet_train_gt_box.json
-├── scannet_val_gt_box.json
-├── scannet_val_pred_box.json
-└── scannet_test_pred_box.json
-```
-
-`scannet_select_frames.json` stores the selected frames for each ScanNet scene.
-
-`pcd_discrete_0.1.pkl` contains discretized point-cloud information used during geometry-aware scene processing and frame selection.
-
-The remaining JSON files contain ground-truth or predicted 3D object-box information for the corresponding ScanNet splits.
-
-## Benchmark Annotations
-
-The `benchmark/` directory documents or stores the source annotations used to construct the processed files. The processed annotations use the Video-3D-LLM LLaVA-style record format, for example: ```json {  "id": "...",  "video": "scannet/scene0249_00",  "conversations": [    {"from": "human", "value": "<image> ..."},    {"from": "gpt", "value": "..."}  ],  "metadata": {"dataset": "scanqa"} } ``` The `video` value is a scene key. It is resolved through the EmbodiedScan scene metadata and does not point to a standalone video file.
-
-```
-benchmark/
-├── Sqa3D/
-├── ScanQA/
-├── ScanRefer/
-├── Scan2Cap/
-└── Multi3DRefer/
-```
-
-| Benchmark    | Task                                  | Original Project                                         |
-| ------------ | ------------------------------------- | -------------------------------------------------------- |
-| SQA3D        | Situated 3D question answering        | [SQA3D](https://github.com/SilongYong/SQA3D)             |
-| ScanQA       | 3D question answering                 | [ScanQA](https://github.com/ATR-DBI/ScanQA)              |
-| ScanRefer    | Single-object referring and grounding | [ScanRefer](https://github.com/daveredrum/ScanRefer)     |
-| Scan2Cap     | 3D object captioning                  | [Scan2Cap](https://github.com/daveredrum/Scan2Cap)       |
-| Multi3DRefer | Multi-object referring and grounding  | [Multi3DRefer](https://github.com/3dlg-hcvc/M3DRef-CLIP) |
-
-## Processed Annotations
-
-The `processed/` directory contains annotations converted into the unified LLaVA-style format used by the GeoGR training and evaluation pipeline.
-
-```
-processed/
-├── full/
-└── subsets/
-```
-
-A processed record follows the general structure:
-
-```
-{
-  "id": "...",
-  "video": "scannet/scene0249_00",
-  "conversations": [
-    {
-      "from": "human",
-      "value": "<image> ..."
-    },
-    {
-      "from": "gpt",
-      "value": "..."
-    }
-  ],
-  "metadata": {
-    "dataset": "scanqa"
-  }
-}
-```
-
-The `video` field represents the corresponding 3D scene key rather than a standalone video file.
-
-### Full Annotations
-
-`processed/full/` contains the complete processed annotations for the five downstream tasks, including the available training and evaluation splits.
-
-```
-processed/
-└── full/
-    ├── sqa3d_*_llava_style.json
-    ├── scanqa_*_llava_style.json
-    ├── scan2cap_*_llava_style.json
-    ├── scanrefer_vg_*_llava_style.json
-    └── multi3drefer_*_llava_style.json
-```
-
-### GeoGR Training Subsets
-
-`processed/subsets/` contains the PRISM-selected training subsets used in the GeoGR post-training experiments.
-
-The subset selection only changes which annotation records are used for training. It does not change the RGB images, depth maps, camera poses, point clouds, scene geometry, or scene IDs.
-
-```
-processed/
-└── subsets/
-    ├── sqa3d_train_llava_style.json
-    ├── scanqa_train_llava_style.json
-    ├── scan2cap_train_llava_style.json
-    ├── scanrefer_vg_train_llava_style.json
-    └── multi3drefer_train_llava_style.json
-```
-
-The released subsets contain approximately 30% of the corresponding full training annotations.
-
-| Task         | Training Records |
-| ------------ | ---------------- |
-| ScanRefer    | 11,000           |
-| Multi3DRefer | 13,151           |
-| Scan2Cap     | 11,000           |
-| ScanQA       | 7,955            |
-| SQA3D        | 23,834           |
-| **Total**    | **66,940**       |
-
-## License and Attribution
-
-This repository is a derived data release and does not supersede or modify the licenses and access conditions of the original datasets.
-
-Users should follow the original terms of ScanNet, EmbodiedScan, SQA3D, ScanQA, ScanRefer, Scan2Cap, and Multi3DRefer when using or redistributing the corresponding data.
-
+---
+license: other
+task_categories:
+- image-text-to-text
+---
+
+# GeoGR-67K-CAT
+
+Paper: [GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs](https://huggingface.co/papers/2610.13035)  
+Code: [https://github.com/EIT-NLP/GeoGR](https://github.com/EIT-NLP/GeoGR)
+
+This repository contains the data used in **GeoGR: Spatial–Semantic Token Compression and Group Routing for Efficient 3D VLMs**.
+
+The release includes ScanNet scene data and derived 3D representations, EmbodiedScan scene metadata, geometry and frame-selection metadata, benchmark annotations for five downstream 3D-language tasks, and the processed training data used in our experiments.
+
+The scene representation and data preprocessing follow the general pipeline of [Video-3D-LLM](https://github.com/LaVi-Lab/Video-3D-LLM).
+
+## Dataset Organization
+
+After extracting the ScanNet pose data, the expected directory structure is:
+
+```
+GeoGR-67K-CAT/
+├── scannet/
+│   ├── scans/
+│   ├── pose/
+│   ├── pcd_with_object_aabbs/
+│   └── mask/
+│
+├── embodiedscan/
+│   ├── embodiedscan_infos_train.pkl
+│   ├── embodiedscan_infos_val.pkl
+│   └── embodiedscan_infos_test.pkl
+│
+├── metadata/
+│   ├── scannet_select_frames.json
+│   ├── pcd_discrete_0.1.pkl
+│   ├── scannet_train_gt_box.json
+│   ├── scannet_val_gt_box.json
+│   ├── scannet_val_pred_box.json
+│   └── scannet_test_pred_box.json
+│
+├── benchmark/
+���   ├── Sqa3D/
+│   ├── ScanQA/
+│   ├── ScanRefer/
+│   ├── Scan2Cap/
+│   └── Multi3DRefer/
+│
+└── processed/
+    ├── full/
+    └── subsets/
+```
+
+## ScanNet Data
+
+The `scannet/` directory contains the ScanNet scene data and derived scene representations used in GeoGR.
+
+### Scans
+
+`scannet/scans/` contains the original ScanNet v2 scene files. Each scene follows the standard ScanNet organization:
+
+```
+scannet/
+└── scans/
+    └── sceneXXXX_XX/
+        ├── sceneXXXX_XX_vh_clean_2.ply
+        ├── sceneXXXX_XX_vh_clean_2.0.010000.segs.json
+        ├── sceneXXXX_XX.aggregation.json
+        ├── sceneXXXX_XX.txt
+        └── sceneXXXX_XX.sens
+```
+
+The original ScanNet v2 data can be obtained from the [official ScanNet website](http://www.scan-net.org/).
+
+### Pose
+
+`scannet/pose/` contains the preprocessed multi-view observations for each scene, including RGB images, depth information, and camera poses.
+
+The pose data are distributed as five split archive files (`posed_images_part_aa` to `posed_images_part_ae`). After downloading, merge and extract them into `scannet/pose/` with:
+
+```
+cd scannet && cat posed_images_part_a{a,b,c,d,e} > posed_images.tar.gz && mkdir -p pose && tar -xzf posed_images.tar.gz -C pose && rm posed_images.tar.gz
+```
+
+### Point Clouds and Object Boxes
+
+`scannet/pcd_with_object_aabbs/` contains processed scene point clouds together with object-level axis-aligned 3D bounding boxes.
+
+### Object Masks
+
+`scannet/mask/` contains the object-mask and proposal information used by the scene processing and evaluation pipeline.
+
+The corresponding ScanNet preprocessing scripts are available in the [Video-3D-LLM 3D preprocessing directory](https://github.com/LaVi-Lab/Video-3D-LLM/tree/main/scripts/3d/preprocessing).
+
+## EmbodiedScan Metadata
+
+The `embodiedscan/` directory contains the scene-information files used to associate multi-view observations with their corresponding 3D scenes.
+
+```
+embodiedscan/
+├── embodiedscan_infos_train.pkl
+├── embodiedscan_infos_val.pkl
+└── embodiedscan_infos_test.pkl
+```
+
+The original EmbodiedScan data can be obtained from the [official EmbodiedScan repository](https://github.com/OpenRobotLab/EmbodiedScan/tree/main/data). Access may require completing the corresponding data request procedure.
+
+## Metadata
+
+The `metadata/` directory contains auxiliary geometry, frame-selection, and object-box information used by the GeoGR data pipeline.
+
+```
+metadata/
+├── scannet_select_frames.json
+├── pcd_discrete_0.1.pkl
+├── scannet_train_gt_box.json
+├── scannet_val_gt_box.json
+├── scannet_val_pred_box.json
+└── scannet_test_pred_box.json
+```
+
+`scannet_select_frames.json` stores the selected frames for each ScanNet scene.
+
+`pcd_discrete_0.1.pkl` contains discretized point-cloud information used during geometry-aware scene processing and frame selection.
+
+The remaining JSON files contain ground-truth or predicted 3D object-box information for the corresponding ScanNet splits.
+
+## Benchmark Annotations
+
+The `benchmark/` directory documents or stores the source annotations used to construct the processed files. The processed annotations use the Video-3D-LLM LLaVA-style record format, for example: ```json {  "id": "...",  "video": "scannet/scene0249_00",  "conversations": [    {"from": "human", "value": "<image> ..."},    {"from": "gpt", "value": "..."}  ],  "metadata": {"dataset": "scanqa"} } ``` The `video` value is a scene key. It is resolved through the EmbodiedScan scene metadata and does not point to a standalone video file.
+
+```
+benchmark/
+├── Sqa3D/
+├── ScanQA/
+├── ScanRefer/
+├── Scan2Cap/
+└── Multi3DRefer/
+```
+
+| Benchmark    | Task                                  | Original Project                                         |
+| ------------ | ------------------------------------- | -------------------------------------------------------- |
+| SQA3D        | Situated 3D question answering        | [SQA3D](https://github.com/SilongYong/SQA3D)             |
+| ScanQA       | 3D question answering                 | [ScanQA](https://github.com/ATR-DBI/ScanQA)              |
+| ScanRefer    | Single-object referring and grounding | [ScanRefer](https://github.com/daveredrum/ScanRefer)     |
+| Scan2Cap     | 3D object captioning                  | [Scan2Cap](https://github.com/daveredrum/Scan2Cap)       |
+| Multi3DRefer | Multi-object referring and grounding  | [Multi3DRefer](https://github.com/3dlg-hcvc/M3DRef-CLIP) |
+
+## Processed Annotations
+
+The `processed/` directory contains annotations converted into the unified LLaVA-style format used by the GeoGR training and evaluation pipeline.
+
+```
+processed/
+├── full/
+└── subsets/
+```
+
+A processed record follows the general structure:
+
+```
+{
+  "id": "...",
+  "video": "scannet/scene0249_00",
+  "conversations": [
+    {
+      "from": "human",
+      "value": "<image> ..."
+    },
+    {
+      "from": "gpt",
+      "value": "..."
+    }
+  ],
+  "metadata": {
+    "dataset": "scanqa"
+  }
+}
+```
+
+The `video` field represents the corresponding 3D scene key rather than a standalone video file.
+
+### Full Annotations
+
+`processed/full/` contains the complete processed annotations for the five downstream tasks, including the available training and evaluation splits.
+
+```
+processed/
+└── full/
+    ├── sqa3d_*_llava_style.json
+    ├── scanqa_*_llava_style.json
+    ├── scan2cap_*_llava_style.json
+    ├── scanrefer_vg_*_llava_style.json
+    └── multi3drefer_*_llava_style.json
+```
+
+### GeoGR Training Subsets
+
+`processed/subsets/` contains the PRISM-selected training subsets used in the GeoGR post-training experiments.
+
+The subset selection only changes which annotation records are used for training. It does not change the RGB images, depth maps, camera poses, point clouds, scene geometry, or scene IDs.
+
+```
+processed/
+└── subsets/
+    ├── sqa3d_train_llava_style.json
+    ├── scanqa_train_llava_style.json
+    ├── scan2cap_train_llava_style.json
+    ├── scanrefer_vg_train_llava_style.json
+    └── multi3drefer_train_llava_style.json
+```
+
+The released subsets contain approximately 30% of the corresponding full training annotations.
+
+| Task         | Training Records |
+| ------------ | ---------------- |
+| ScanRefer    | 11,000           |
+| Multi3DRefer | 13,151           |
+| Scan2Cap     | 11,000           |
+| ScanQA       | 7,955            |
+| SQA3D        | 23,834           |
+| **Total**    | **66,940**       |
+
+## License and Attribution
+
+This repository is a derived data release and does not supersede or modify the licenses and access conditions of the original datasets.
+
+Users should follow the original terms of ScanNet, EmbodiedScan, SQA3D, ScanQA, ScanRefer, Scan2Cap, and Multi3DRefer when using or redistributing the corresponding data.
+
 Please retain the original dataset attribution and license notices when creating mirrors or derivative releases.