The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Fire3D Processed Inference Data
Fire3D is a unified feed-forward framework that transforms a single RGB image or casual RGB video into simulation-ready 3D scene assets. It predicts a compositional scene representation with object-level 6-DoF pose, bounding box, mesh geometry, and texture, without test-time optimization.
This release provides the inference code, model checkpoints, processed example inputs, and frozen protocols needed to reproduce Fire3D results on iTHOR, Imaginarium, ScanNet++, and single-image scenes.
This dataset contains only processed, inference-ready inputs selected by the Fire3D release whitelists. Model files are hosted at hongchi/Fire3D, and code is maintained at xiahongchi/Fire3D.
This repository is not a training-data release. Fire3D does not redistribute its training corpora; all training sources remain available from their public projects under their original terms. For object training data, use TRELLIS-500K. Official scene-data links and Fire3D preprocessing entry points are listed in the code release.
Overview
Teaser. Fire3D reconstructs a compositional 3D scene from a single RGB image or casual RGB video. The output contains object-level pose, geometry, and material assets that can be rendered, edited, and used in simulation.
Method overview. Fire3D lifts RGB-D observations into a shared 3D scene representation, predicts object instances and 6-DoF poses, and reconstructs the sparse structure, shape, and PBR fields with three cascaded flow-matching models. Batched decoders and mesh post-processing produce the final textured scene assets.
HC-VAE. The Hierarchical Compression VAE reduces each sparse SC-VAE shape
or material field from a 32^3 x 32 representation to an 8^3 x 64 latent.
This 32x reduction makes scene-level batched flow sampling practical while
retaining the frozen TRELLIS.2 decoding path.
Contents
| Dataset adapter | Whitelisted scenes | Published input |
|---|---|---|
| iTHOR | 67 | 60-view RGB-D sequence, masks, camera, transform, updated exact-camera RGB |
| Imaginarium | 120 | 60-view RGB-D sequence, masks, camera, transform, updated exact-camera RGB |
| ScanNet++ | 165 | up to 300 RGB frames, cameras, Pi3 depth and confidence |
| Single image | 20 | RGB, aligned point cloud, compact native/reconstruction camera record |
The root manifest.json maps every dataset and scene to a deterministic TAR
archive and records its SHA-256 digest. Per-scene archives keep the Hugging Face
repository below its practical file-count limit while allowing selective
downloads. The Fire3D downloader verifies and safely extracts each archive.
Download
git clone https://github.com/xiahongchi/Fire3D.git
cd Fire3D
bash scripts/install.sh
conda activate fire3d
fire3d download --data --dataset ithor --scene-id iTHOR_FloorPlan312_physics
fire3d download --data --dataset imaginarium --scene-id bedroom_01
fire3d download --data --dataset scannetpp --scene-id 09bced689e
fire3d download --data --dataset single_image --scene-id 003025
fire3d download --evaluation shaper
Omit --scene-id to download all published scenes for one adapter. Data is
extracted under data/{ithor,Imaginarium,scannetpp,single_image}.
The optional ShapeR evaluation bundle is extracted under
data/evaluation/shaper/. It is a compact, pickle-free derivative containing
the meshes, bounds, transforms, and condition points required by Fire3D's
geometry evaluator. It retains the upstream ShapeR evaluation-data terms and
includes the source license.
Processing
iTHOR and Imaginarium use the exact-camera RGB rerenders validated by the release protocol. ScanNet++ uses Pi3 depth with retained confidence and drops pixels below confidence 0.6 rather than filling invalid depth. The single-image adapter uses the source camera pose and an aligned point cloud. Fire3D room-box fitting is an inference operation and does not modify the published source points.
Sources And Terms
The archives are processed subsets of iTHOR/AI2-THOR, Imaginarium, ScanNet++,
and TianhangCheng7/Fire3DSingleImageData. They remain subject to their source
datasets' licenses, access conditions, and citation requirements. This dataset
card does not replace or broaden those terms. Users are responsible for
checking the upstream terms before downloading, redistributing, or using a
subset.
Citation
Please cite Fire3D and the source dataset corresponding to each subset used.
@misc{xia2026fire3d,
title={FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute},
author={Hongchi Xia and Tianhang Cheng and Wei-Chiu Ma and Shenlong Wang},
year={2026}
}
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