Add Fire3D model card

README.md

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+---
+license: other
+library_name: pytorch
+pipeline_tag: image-to-3d
+tags:
+  - 3d-scene-reconstruction
+  - rgb-d
+  - single-image
+  - flow-matching
+  - textured-mesh
+---
+
+# FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute
+
+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.
+
+The inference code and installation instructions are maintained at
+[xiahongchi/Fire3D](https://github.com/xiahongchi/Fire3D). Processed release
+inputs are hosted at
+[datasets/hongchi/Fire3D](https://huggingface.co/datasets/hongchi/Fire3D).
+
+## Model Bundle
+
+The repository contains the scene perception checkpoint, the three
+flow-matching checkpoints, sparse VAE decoders, latent normalization
+statistics, and the TRELLIS.2 shape/PBR decoders used by the frozen release
+protocols. `manifest.json` records the byte size and SHA-256 digest of every
+inference file; `checksums.sha256` provides the same values in standard form.
+
+Fire3D's Hierarchical Compression VAE (HC-VAE) compresses each sparse SC-VAE
+shape and material field from a `32^3` representation with 32 channels into an
+`8^3` representation with 64 channels. This 32x reduction enables batched flow
+sampling and VAE decoding across many scene instances while retaining the
+frozen TRELLIS.2 SC-VAE decoding path.
+
+| Component | Bundle path |
+|---|---|
+| Scene perception | `perception/model.pt` |
+| Sparse-structure flow | `reconstruction/flows/ss/model.pt` |
+| Shape flow | `reconstruction/flows/shape/model.pt` |
+| PBR flow | `reconstruction/flows/pbr/model.pt` |
+| Sparse-structure VAE | `reconstruction/vae/ss/ckpts/decoder.pt` |
+| Shape HC-VAE | `reconstruction/vae/shape/ckpts/decoder.pt` |
+| PBR HC-VAE | `reconstruction/vae/pbr/ckpts/decoder.pt` |
+| DINOv3 encoder | `external/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth` |
+| TRELLIS.2 shape decoder | `external/trellis2/shape_dec_next_dc_f16c32_fp16.safetensors` |
+| TRELLIS.2 PBR decoder | `external/trellis2/tex_dec_next_dc_f16c32_fp16.safetensors` |
+
+Public filenames intentionally do not encode private training iteration
+numbers. The current method and HC-VAE figures are available in the
+[GitHub release](https://github.com/xiahongchi/Fire3D#method).
+
+The default protocols use classifier-free guidance 3, flow and VAE batches of
+16, and the batch CuMesh postprocessor. The iTHOR, Imaginarium, and ScanNet++
+protocols enable the validated background room-box fit before background
+reconstruction.
+
+## Use
+
+```bash
+git clone https://github.com/xiahongchi/Fire3D.git
+cd Fire3D
+bash scripts/install.sh
+conda activate fire3d
+bash scripts/install_blender.sh
+
+fire3d download --models
+fire3d download --data --dataset ithor --scene-id iTHOR_FloorPlan312_physics
+bash scripts/run_ithor_example.sh
+```
+
+The downloader places this repository under `checkpoints/Fire3D/` and verifies
+the release manifest before inference.
+
+## Scope
+
+The released checkpoints are intended for inference through the frozen Fire3D
+protocols. Performance can degrade for scenes with inaccurate camera poses or
+depth, severe occlusion, unusual scale, non-room backgrounds, or objects far
+outside the training distribution. The generated meshes should be reviewed
+before use in safety-critical or physically deployed applications.
+
+## Licenses
+
+Original Fire3D code is MIT licensed. This model bundle includes DINOv3 and
+selected TRELLIS.2 files, which retain their upstream licenses. Review the
+included `licenses/` directory and the repository's
+`THIRD_PARTY_NOTICES.md` before use or redistribution.
+
+## Citation
+
+```bibtex
+@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}
+}
+```