Datasets:

RukawaY
/

gs_scenes

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
category_to_task_category_id: struct<car: int64, bench: int64, tree: int64, street lamp: int64, traffic sign: int64, fire hydrant: (... 260 chars omitted)
  child 0, car: int64
  child 1, bench: int64
  child 2, tree: int64
  child 3, street lamp: int64
  child 4, traffic sign: int64
  child 5, fire hydrant: int64
  child 6, trash can: int64
  child 7, bicycle: int64
  child 8, potted plant: int64
  child 9, barrier: int64
  child 10, statue: int64
  child 11, chair: int64
  child 12, sofa: int64
  child 13, bed: int64
  child 14, dining table: int64
  child 15, toilet: int64
  child 16, sink: int64
  child 17, tv: int64
  child 18, refrigerator: int64
  child 19, bookshelf: int64
  child 20, cabinet: int64
  child 21, lamp: int64
category_to_scene_annotation_category_id: struct<car: int64, bench: int64, tree: int64, street lamp: int64, traffic sign: int64, fire hydrant: (... 260 chars omitted)
  child 0, car: int64
  child 1, bench: int64
  child 2, tree: int64
  child 3, street lamp: int64
  child 4, traffic sign: int64
  child 5, fire hydrant: int64
  child 6, trash can: int64
  child 7, bicycle: int64
  child 8, potted plant: int64
  child 9, barrier: int64
  child 10, statue: int64
  child 11, chair: int64
  child 12, sofa: int64
  child 13, bed: int64
  child 14, dining table: int64
  child 15, toilet: int64
  child 16, sink: int64
  child 17, tv: int64
  child 18, refrigerator: int64
  child 19, bookshelf: int64
  child 20, cabinet: int64
  child 21, lamp: int64
goals_
...
y: string
          child 4, position: list<item: double>
              child 0, item: double
          child 5, view_points: list<item: struct<agent_state: struct<position: list<item: double>, rotation: list<item: double>>, i (... 12 chars omitted)
              child 0, item: struct<agent_state: struct<position: list<item: double>, rotation: list<item: double>>, iou: double>
                  child 0, agent_state: struct<position: list<item: double>, rotation: list<item: double>>
                      child 0, position: list<item: double>
                          child 0, item: double
                      child 1, rotation: list<item: double>
                          child 0, item: double
                  child 1, iou: double
episodes: list<item: struct<episode_id: string, scene_id: string, start_position: list<item: double>, start_ro (... 149 chars omitted)
  child 0, item: struct<episode_id: string, scene_id: string, start_position: list<item: double>, start_rotation: lis (... 137 chars omitted)
      child 0, episode_id: string
      child 1, scene_id: string
      child 2, start_position: list<item: double>
          child 0, item: double
      child 3, start_rotation: list<item: double>
          child 0, item: double
      child 4, object_category: string
      child 5, goals: list<item: null>
          child 0, item: null
      child 6, info: struct<geodesic_distance: double>
          child 0, geodesic_distance: double
      child 7, scene_dataset_config: string
to
{'episodes': List({'episode_id': Value('string'), 'scene_id': Value('string'), 'scene_dataset_config': Value('string'), 'start_position': List(Value('float64')), 'start_rotation': List(Value('float64')), 'info': {'geodesic_distance': Value('float64')}, 'goals': List({'position': List(Value('float64')), 'radius': Value('float64')}), 'start_room': Value('null'), 'shortest_paths': Value('null')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              category_to_task_category_id: struct<car: int64, bench: int64, tree: int64, street lamp: int64, traffic sign: int64, fire hydrant: (... 260 chars omitted)
                child 0, car: int64
                child 1, bench: int64
                child 2, tree: int64
                child 3, street lamp: int64
                child 4, traffic sign: int64
                child 5, fire hydrant: int64
                child 6, trash can: int64
                child 7, bicycle: int64
                child 8, potted plant: int64
                child 9, barrier: int64
                child 10, statue: int64
                child 11, chair: int64
                child 12, sofa: int64
                child 13, bed: int64
                child 14, dining table: int64
                child 15, toilet: int64
                child 16, sink: int64
                child 17, tv: int64
                child 18, refrigerator: int64
                child 19, bookshelf: int64
                child 20, cabinet: int64
                child 21, lamp: int64
              category_to_scene_annotation_category_id: struct<car: int64, bench: int64, tree: int64, street lamp: int64, traffic sign: int64, fire hydrant: (... 260 chars omitted)
                child 0, car: int64
                child 1, bench: int64
                child 2, tree: int64
                child 3, street lamp: int64
                child 4, traffic sign: int64
                child 5, fire hydrant: int64
                child 6, trash can: int64
                child 7, bicycle: int64
                child 8, potted plant: int64
                child 9, barrier: int64
                child 10, statue: int64
                child 11, chair: int64
                child 12, sofa: int64
                child 13, bed: int64
                child 14, dining table: int64
                child 15, toilet: int64
                child 16, sink: int64
                child 17, tv: int64
                child 18, refrigerator: int64
                child 19, bookshelf: int64
                child 20, cabinet: int64
                child 21, lamp: int64
              goals_
              ...
              y: string
                        child 4, position: list<item: double>
                            child 0, item: double
                        child 5, view_points: list<item: struct<agent_state: struct<position: list<item: double>, rotation: list<item: double>>, i (... 12 chars omitted)
                            child 0, item: struct<agent_state: struct<position: list<item: double>, rotation: list<item: double>>, iou: double>
                                child 0, agent_state: struct<position: list<item: double>, rotation: list<item: double>>
                                    child 0, position: list<item: double>
                                        child 0, item: double
                                    child 1, rotation: list<item: double>
                                        child 0, item: double
                                child 1, iou: double
              episodes: list<item: struct<episode_id: string, scene_id: string, start_position: list<item: double>, start_ro (... 149 chars omitted)
                child 0, item: struct<episode_id: string, scene_id: string, start_position: list<item: double>, start_rotation: lis (... 137 chars omitted)
                    child 0, episode_id: string
                    child 1, scene_id: string
                    child 2, start_position: list<item: double>
                        child 0, item: double
                    child 3, start_rotation: list<item: double>
                        child 0, item: double
                    child 4, object_category: string
                    child 5, goals: list<item: null>
                        child 0, item: null
                    child 6, info: struct<geodesic_distance: double>
                        child 0, geodesic_distance: double
                    child 7, scene_dataset_config: string
              to
              {'episodes': List({'episode_id': Value('string'), 'scene_id': Value('string'), 'scene_dataset_config': Value('string'), 'start_position': List(Value('float64')), 'start_rotation': List(Value('float64')), 'info': {'geodesic_distance': Value('float64')}, 'goals': List({'position': List(Value('float64')), 'radius': Value('float64')}), 'start_room': Value('null'), 'shortest_paths': Value('null')})}
              because column names don't match

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A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting

ECCV 2026

Ziyuan Xia • Jingyi Xu • Chong Cui • Yuanhong Yu • Jiazhao Zhang • Qingsong Yan • Tao Ni
Junbo Chen • Xiaowei Zhou • Hujun Bao • Ruizhen Hu • Sida Peng

🤗 About This Dataset

This is the official GS dataset for Habitat-GS, a high-fidelity embodied navigation simulator built on 3D Gaussian Splatting and dynamic gaussian avatars. The dataset contains 129 indoor/outdoor 3DGS scenes, along with 6 gaussian avatar assets, pre-generated navigation episodes, dynamic-navigation data and VLN trajectory data for StreamVLN and Uni-NaVid — everything needed to train and evaluate embodied navigation agents in high-fidelity Gaussian Splatting environments!

Key statistics:

	Train	Val	Total
Self-reconstructed scenes (`scene01`–`scene65`)	55 (`scene01`–`scene55`)	10 (`scene56`–`scene65`)	65
InteriorGS scenes (`interior_*`)	55	9	64
All scenes	110	19	129
PointNav episodes	110,000	1,900	111,900
ImageNav episodes	110,000	1,900	111,900
ObjectNav episodes	110,000	1,900	111,900
VLN episodes	22,000	950	22,950
Dynamic-nav episodes (on 10 sample scenes, scene01-scene10)	1,000	100	1,100

Each self-reconstructed scene (scene01–scene65) comes with a 3DGS render asset (<scene>.gs.ply), a collision mesh (<scene>.mesh.ply), and a navigation mesh (<scene>.navmesh). Each InteriorGS scene (interior_*) only ships 3DGS and navmesh — <scene>.gs.ply + <scene>.navmesh. The dataset also includes 6 gaussian avatars exported from AnimatableGaussians, with SMPL/SMPL-X body models for motion driving.

Note: Due to license constraints, SMPL and SMPL-X body models are not included in this dataset. To use the dynamic avatars, please register and accept the licenses, then download and unzip the models into avatars/{smpl,smplx}/:

SMPL-X — register at https://smpl-x.is.tue.mpg.de, download models_smplx_v1_1.zip
SMPL — register at https://smpl.is.tue.mpg.de, download SMPL_python_v.1.1.0.zip

🏛️ Dataset Layout

The dataset is organized into six independent categories that can be downloaded separately:

	Category	Size	Required For
1	GS Scenes (`train/`, `val/`)	~27 GB	Everything — core scene assets
2	Gaussian Avatars (`avatars/`)	~3.1 GB	Dynamic avatar simulation
3	Habitat-Lab Nav Data (`configs/`, `episodes/{pointnav,imagenav,objectnav}/`)	~30 MB	PointNav / ImageNav / ObjectNav training & evaluation
4	StreamVLN Data (`configs/`, `episodes/vln/`, `trajectory_data/vln/`)	~40 GB	VLN training & evaluation (StreamVLN)
5	Uni-NaVid Data (`configs/`, `episodes/vln/`, `trajectory_data/uninavid/`)	~25 GB	VLN training & evaluation (Uni-NaVid)
6	Dynamic Nav Data (`configs/`, `dynamic_nav/`)	~25 MB	Dynamic navigation — avatar avoidance & tracking

Dataset layout:

.
├── train.scene_dataset_config.json       # Habitat scene dataset config (train)
├── val.scene_dataset_config.json         # Habitat scene dataset config (val)
│
├── train/                                # [Category 1] 110 training GS scenes (~24 GB)
│   ├── scene01/                          #   self-reconstructed (full assets)
│   │   ├── scene01.gs.ply               #     3DGS render asset
│   │   ├── scene01.mesh.ply             #     collision mesh
│   │   └── scene01.navmesh              #     navigation mesh
│   ├── scene02/ ... scene55/             #   55 self-reconstructed scenes total
│   ├── interior_0007_840137/             #   InteriorGS (only 3DGS and navmesh)
│   │   ├── interior_0007_840137.gs.ply  #     3DGS render asset
│   │   └── interior_0007_840137.navmesh #     navigation mesh
│   └── interior_0022_840117/ ... ×55     #   55 InteriorGS scenes total
│
├── val/                                  # [Category 1] 19 evaluation GS scenes (~3.3 GB)
│   ├── scene56/ ... scene65/             #   10 self-reconstructed val scenes
│   └── interior_0516_840045/ ... ×9      #   9 InteriorGS val scenes
│
├── avatars/                              # [Category 2] Gaussian avatar assets (~3.1 GB)
│   ├── README.md                         #   how to obtain the SMPL/SMPL-X body models (see below)
│   ├── avatar1/                          #   canonical gaussians of gaussian avatars
│   │   └── canonical_gs.npz
│   ├── avatar2/ ... avatar8/
│   ├── smpl/                             #   SMPL body models — NOT included (license); download yourself
│   │   └── SMPL_{NEUTRAL,MALE,FEMALE}.pkl
│   └── smplx/                            #   SMPL-X body models — NOT included (license); download yourself
│       └── SMPLX_{NEUTRAL,MALE,FEMALE}.{npz,pkl}
│
├── configs/                              # [Category 3, 4, 5 & 6] Hydra YAML configs (~64 KB)
│   ├── ddppo_pointnav_gs_{train,eval}.yaml
│   ├── ddppo_imagenav_gs_{train,eval}.yaml
│   ├── ddppo_objectnav_gs_{train,eval}.yaml
│   ├── ddppo_dynamic_track_gs_{train,eval}.yaml            #   dynamic nav: human tracking
│   ├── ddppo_dynamic_avoid_gs_{train,eval}.yaml            #   dynamic nav: PointNav + avoidance
│   ├── ddppo_dynamic_avoid_imagenav_gs_{train,eval}.yaml   #   dynamic nav: ImageNav + avoidance
│   ├── ddppo_dynamic_avoid_objectnav_gs_{train,eval}.yaml  #   dynamic nav: ObjectNav + avoidance
│   ├── vln_gs_eval.yaml                  #   StreamVLN eval config (hfov=79, turn=15)
│   └── vln_uninavid_gs_eval.yaml         #   Uni-NaVid eval config (hfov=120, turn=30)
│
├── episodes/                             # [Category 3, 4 & 5] Navigation episodes (~80 MB)
│   ├── pointnav/{train,val}/             #   PointNav: 110,000 train + 1,900 val
│   ├── imagenav/{train,val}/             #   ImageNav: 110,000 train + 1,900 val
│   ├── objectnav/{train,val}/            #   ObjectNav: 110,000 train + 1,900 val
│   └── vln/{train,val}/                  #   VLN: 22,000 train + 950 val
│
├── dynamic_nav/                          # [Category 6] Dynamic navigation data (~25 MB)
│   ├── dynamic_nav.scene_dataset_config.json  # Habitat scene dataset config (10 dynamic scenes)
│   ├── scenes/                           #   scene_instance.json per scene: stage + navmesh +
│   │   └── <scene>.scene_instance.json   #     gaussian_avatars wiring (avatar, offset_y, scale)
│   ├── stages/                           #   GS stage templates
│   │   └── <scene>.stage_config.json
│   ├── trajectories/                     #   GAMMA-generated avatar walks (joint_mats + proxy_capsules)
│   │   └── <scene>.driver.pkl            #     one walking avatar per scene, scene01–scene10
│   ├── episodes/{train,val}/             #   PointNav format: 1,000 train + 100 val; agent spawns
│   │                                     #     near the avatar (shared by avoid/imagenav/tracking)
│   └── episodes_objectnav/{train,val}/   #   ObjectNav format: 1,000 train + 100 val
│
└── trajectory_data/                      # [Category 4 & 5] VLN trajectory data
    ├── vln/                              #   StreamVLN trajectories (~40 GB)
    │   ├── annotations.json              #     action sequences + instructions (train)
    │   ├── annotations_val.json          #     action sequences + instructions (val)
    │   └── images/                       #     per-scene tar archives (extract before use)
    │       ├── scene01.tar               #       scene01 trajectories
    │       ├── interior_0007_840137.tar  #       interior_0007 trajectories
    │       └── ...                       #       129 per-scene archives, 22,950 trajectories total
    └── uninavid/                         #   Uni-NaVid trajectories (~25 GB)
        ├── nav_gs_train.json             #     conversation-format annotations (train)
        ├── nav_gs_val.json               #     conversation-format annotations (val)
        └── nav_videos/                   #     per-scene tar archives of .mp4 videos
            ├── scene01.tar               #       scene01 videos
            ├── interior_0007_840137.tar  #       interior_0007 videos
            └── ...                       #       129 per-scene archives, 22,950 videos total

🎒 Selective Download

You can download one or more categories using huggingface_hub's allow_patterns / ignore_patterns:

from huggingface_hub import snapshot_download

REPO = "RukawaY/gs_scenes"
LOCAL = "data/scene_datasets/gs_scenes"

# ── Download only GS scenes ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["train/**", "val/**", "*.scene_dataset_config.json"])

# ── Download GS scenes + avatars ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["train/**", "val/**", "*.scene_dataset_config.json", "avatars/**"])

# ── Download everything for Habitat-Lab navigation tasks ──
snapshot_download(REPO, local_dir=LOCAL,
    ignore_patterns=["trajectory_data/**", "avatars/**", "episodes/vln/**"])

# ── Download everything for StreamVLN ──
snapshot_download(REPO, local_dir=LOCAL,
    ignore_patterns=["avatars/**", "episodes/pointnav/**", "episodes/imagenav/**",
                     "episodes/objectnav/**", "trajectory_data/uninavid/**"])

# ── Download everything for Uni-NaVid ──
snapshot_download(REPO, local_dir=LOCAL,
    ignore_patterns=["avatars/**", "episodes/pointnav/**", "episodes/imagenav/**",
                     "episodes/objectnav/**", "trajectory_data/vln/**"])

# ── Download everything for dynamic navigation ──
# needs the 10 scenes (scene01–scene10) + avatars + dynamic_nav data + configs
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["train/scene0*/**", "train/scene10/**", "*.scene_dataset_config.json",
                    "avatars/**", "dynamic_nav/**", "configs/**"])

# ── Download a few specific scenes' trajectories (StreamVLN) ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["trajectory_data/vln/annotations*.json",
                    "trajectory_data/vln/images/scene01.tar",
                    "trajectory_data/vln/images/interior_0007_840137.tar"])

# ── Download a few specific scenes' trajectories (Uni-NaVid) ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["trajectory_data/uninavid/nav_gs_*.json",
                    "trajectory_data/uninavid/nav_videos/scene01.tar",
                    "trajectory_data/uninavid/nav_videos/interior_0007_840137.tar"])

# ── Download everything (~95 GB) ──
snapshot_download(REPO, local_dir=LOCAL)

After downloading trajectory archives, extract per-scene trajectories in place:

# StreamVLN trajectories
cd data/scene_datasets/gs_scenes/trajectory_data/vln/images
for f in *.tar; do tar xf "$f" && rm "$f"; done

# Uni-NaVid trajectories
cd data/scene_datasets/gs_scenes/trajectory_data/uninavid/nav_videos
for f in *.tar; do tar xf "$f" && rm "$f"; done

🚖 Placement

Place the downloaded data under habitat-gs/data/scene_datasets/gs_scenes/ so that the directory structure matches the layout above. The Habitat configs and training/evaluation scripts in Habitat-GS expect this exact path. See the Habitat-GS README for full setup and usage instructions.

📙 Citation

If you find Habitat-GS useful in your research, please consider citing:

@inproceedings{xia2026habitat,
  title={Habitat-gs: A high-fidelity navigation simulator with dynamic gaussian splatting},
  author={Xia, Ziyuan and Xu, Jingyi and Cui, Chong and Yu, Yuanhong and Zhang, Jiazhao and Yan, Qingsong and Ni, Tao and Chen, Junbo and Zhou, Xiaowei and Bao, Hujun and others},
  booktitle={European Conference on Computer Vision},
  pages={306--323},
  year={2026},
  organization={Springer}
}