Datasets:

hongchi
/

Fire3D

Tasks:

Image-to-3D

ArXiv:

Tags:

3d-scene-reconstruction

License:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

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

Paper | Code | Models

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.

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.

@article{xia2026fire3d,
  title={{FIRE3D}: Feed-forward Interactive 3D Scene Reconstruction Within A Minute},
  author={Xia, Hongchi and Cheng, Tianhang and Ma, Wei-Chiu and Wang, Shenlong},
  journal={arXiv preprint arXiv:2609.08848},
  year={2026},
  url={https://arxiv.org/pdf/2609.08848}
}