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 147, 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 127, 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 478, 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 foreground 3DGS render asset (<scene>.gs.ply), an optional background 3DGS asset (background.ply, can use tool script to merge with foreground GS), 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               #     foreground 3DGS render asset
│   │   ├── background.ply               #     background 3DGS asset (sky / distant geometry; optional)
│   │   ├── 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:

@misc{xia2026habitatgs,
    title={Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting}, 
    author={Ziyuan Xia and Jingyi Xu and Chong Cui and Yuanhong Yu and Jiazhao Zhang and Qingsong Yan and Tao Ni and Junbo Chen and Xiaowei Zhou and Hujun Bao and Ruizhen Hu and Sida Peng},
    year={2026},
    eprint={2604.12626},
    archivePrefix={arXiv},
    primaryClass={cs.RO},
    url={https://arxiv.org/abs/2604.12626}, 
}