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: (... 107 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
category_to_scene_annotation_category_id: struct<car: int64, bench: int64, tree: int64, street lamp: int64, traffic sign: int64, fire hydrant: (... 107 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
goals_by_category: struct<scene01.gs.ply_barrier: list<item: struct<object_id: string, object_name: string, object_name (... 2218 chars omitted)
  child 0, scene01.gs.ply_barrier: list<item: struct<object_id: string, object_name: string, object_name_id: int64, object_category: st (... 163 chars omitted)
      child 0, item: struct<object_id: string, object_name: string, object_name_id: int64, object_category: string, posit (... 151 chars omitted)
          child 0, object_id: string
          child 1, object_name: string
     
...
_id: int64
          child 3, object_category: 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 (... 119 chars omitted)
  child 0, item: struct<episode_id: string, scene_id: string, start_position: list<item: double>, start_rotation: lis (... 107 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
to
{'episodes': List({'episode_id': Value('string'), 'scene_id': Value('string'), 'start_position': List(Value('float64')), 'start_rotation': List(Value('float64')), 'goals': List({'position': List(Value('float64')), 'radius': Value('float64')}), 'info': {'geodesic_distance': Value('float64')}})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              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: (... 107 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
              category_to_scene_annotation_category_id: struct<car: int64, bench: int64, tree: int64, street lamp: int64, traffic sign: int64, fire hydrant: (... 107 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
              goals_by_category: struct<scene01.gs.ply_barrier: list<item: struct<object_id: string, object_name: string, object_name (... 2218 chars omitted)
                child 0, scene01.gs.ply_barrier: list<item: struct<object_id: string, object_name: string, object_name_id: int64, object_category: st (... 163 chars omitted)
                    child 0, item: struct<object_id: string, object_name: string, object_name_id: int64, object_category: string, posit (... 151 chars omitted)
                        child 0, object_id: string
                        child 1, object_name: string
                   
              ...
              _id: int64
                        child 3, object_category: 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 (... 119 chars omitted)
                child 0, item: struct<episode_id: string, scene_id: string, start_position: list<item: double>, start_rotation: lis (... 107 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
              to
              {'episodes': List({'episode_id': Value('string'), 'scene_id': Value('string'), 'start_position': List(Value('float64')), 'start_rotation': List(Value('float64')), 'goals': List({'position': List(Value('float64')), 'radius': Value('float64')}), 'info': {'geodesic_distance': Value('float64')}})}
              because column names don't match

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A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting

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 47 indoor/outdoor GS scenes reconstructed from real-world environments, along with 6 gaussian avatar assets, pre-generated navigation episodes, 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
Scenes	42 (scene01–scene42)	5 (scene43–scene47)	47
PointNav episodes	42,000	500	42,500
ImageNav episodes	42,000	500	42,500
ObjectNav episodes	42,000	500	42,500
VLN episodes	8,400	250	8,650

Each scene consists of a 3DGS render asset (.gs.ply), a collision mesh (.mesh.ply), and a navigation mesh (.navmesh). The dataset also includes 6 gaussian avatars exported from AnimatableGaussians, with SMPL/SMPL-X body models for motion driving.

🏛️ Dataset Layout

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

	Category	Size	Required For
1	GS Scenes (`train/`, `val/`)	~7.2 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}/`)	~11 MB	PointNav / ImageNav / ObjectNav training & evaluation
4	StreamVLN Data (`configs/`, `episodes/vln/`, `trajectory_data/vln/`)	~50 GB	VLN training & evaluation (StreamVLN)
5	Uni-NaVid Data (`configs/`, `episodes/vln/`, `trajectory_data/uninavid/`)	~21 GB	VLN training & evaluation (Uni-NaVid)

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] 42 training GS scenes (~6.5 GB)
│   ├── scene01/
│   │   ├── scene01.gs.ply               #   3DGS render asset
│   │   ├── scene01.mesh.ply             #   collision mesh
│   │   └── scene01.navmesh              #   navigation mesh
│   └── scene02/ ... scene42/
│
├── val/                                  # [Category 1] 5 evaluation GS scenes (~712 MB)
│   └── scene43/ ... scene47/
│
├── avatars/                              # [Category 2] Gaussian avatar assets (~3.1 GB)
│   ├── avatar1/                          #   canonical gaussians of gaussian avatars
│   │   └── canonical_gs.npz
│   ├── avatar2/ ... avatar8/
│   ├── smpl/                             #   SMPL body models
│   │   ├── SMPL_FEMALE.pkl
│   │   ├── SMPL_MALE.pkl
│   │   └── SMPL_NEUTRAL.pkl
│   └── smplx/                            #   SMPL-X body models
│       ├── SMPLX_FEMALE.{npz,pkl}
│       ├── SMPLX_MALE.{npz,pkl}
│       └── SMPLX_NEUTRAL.{npz,pkl}
│
├── configs/                              # [Category 3, 4 & 5] Hydra YAML configs (~32 KB)
│   ├── ddppo_pointnav_gs_{train,eval}.yaml
│   ├── ddppo_imagenav_gs_{train,eval}.yaml
│   ├── ddppo_objectnav_gs_{train,eval}.yaml
│   ├── 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 (~42 MB)
│   ├── pointnav/{train,val}/             #   PointNav: 42,000 train + 500 val
│   ├── imagenav/{train,val}/             #   ImageNav: 42,000 train + 500 val
│   ├── objectnav/{train,val}/            #   ObjectNav: 42,000 train + 500 val
│   └── vln/{train,val}/                  #   VLN: 8,400 train + 250 val
│
└── trajectory_data/                      # [Category 4 & 5] VLN trajectory data
    ├── vln/                              #   StreamVLN trajectories (~50 GB)
    │   ├── annotations.json              #     action sequences + instructions
    │   └── images/                       #     rendered RGB frames (per-scene tar archives)
    │       ├── scene01.tar               #       scene01 trajectories
    │       └── ...                       #       47 per-scene archives
    └── uninavid/                         #   Uni-NaVid trajectories (~21 GB)
        ├── nav_gs_train.json             #     conversation-format annotations (train)
        ├── nav_gs_val.json               #     conversation-format annotations (val)
        └── nav_videos/                   #     .mp4 trajectory videos (per-scene tar archives)
            ├── scene01.tar               #       scene01 videos
            └── ...                       #       47 per-scene archives, 8,650 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 specific scene trajectories only (StreamVLN) ──
snapshot_download(REPO, local_dir=LOCAL,
    allow_patterns=["trajectory_data/vln/annotations.json",
                    "trajectory_data/vln/images/scene01.tar",
                    "trajectory_data/vln/images/scene02.tar"])

# ── Download specific scene trajectories only (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/scene02.tar"])

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

After downloading trajectory archives, extract per-scene trajectories:

# 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}, 
}

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Paper for RukawaY/gs_scenes

Habitat-GS: A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting

Paper • 2604.12626 • Published 1 day ago • 11