Duplicate from GwanHyeong/InSpace

Co-authored-by: Koo <GwanHyeong@users.noreply.huggingface.co>

README.md

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+---
+license: mit
+pipeline_tag: image-to-3d
+library_name: trellis2
+language:
+- en
+tags:
+- image-to-3d
+- 3d-scene-generation
+- indoor-scene
+- 360-image
+- panorama
+- equirectangular
+- inspace
+- eccv2026
+---
+
+# InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360° Image
+
+<p align="center">
+<a href="https://arxiv.org/abs/2607.03990"><img src="https://img.shields.io/badge/Paper-Arxiv-b31b1b.svg" alt="Paper"></a>
+<a href="https://kookie12.github.io/InSpace-Project-Page/"><img src="https://img.shields.io/badge/Project-Website-blue" alt="Project Page"></a>
+<a href="https://github.com/kookie12/InSpace"><img src="https://img.shields.io/badge/Code-GitHub-black" alt="Code"></a>
+<a href="https://huggingface.co/datasets/GwanHyeong/ERP-FRONT-30K"><img src="https://img.shields.io/badge/Hugging%20Face-Dataset-orange" alt="Dataset"></a>
+</p>
+
+![InSpace teaser](figures/teaser.png)
+
+**Venue:** ECCV 2026 · Malmö, Sweden
+
+## Introduction
+
+**InSpace** generates a complete, **asset-aware** 3D indoor scene from a **single 360°
+(equirectangular) panorama**, producing a full-room mesh along with individual, separable, textured
+furniture meshes. It is built on the [TRELLIS.2](https://huggingface.co/microsoft/TRELLIS.2-4B)
+O-Voxel representation and extends it with a panorama-native, **structure-aware** generation
+pipeline: view-selective cross-attention driven by the camera center, layout-guided structure
+inversion from monocular depth, a 3D bounding-box estimator, and asset-aware shape and texture
+generation with global-local hybrid attention.
+
+Concretely, the scene is produced in three cascaded flow-matching stages: (1) estimating **Partial
+Scene Geometry (PSG)** as a spatial prior, (2) generating **coarse scene structure (CSG)** with
+view-selective cross-attention, and (3) producing **detailed layout and asset geometry with
+textures** through a global-local hybrid attention.
+
+## Model Details
+*   **Model Type:** Three-stage flow-matching framework on O-Voxel structured latents, with a
+    CenterPoint-based 3D bounding-box estimator
+*   **Input:** Single 360° equirectangular (ERP) panorama
+*   **Output:** Complete 3D indoor scene (structural layout + separable, textured asset meshes with
+    PBR materials)
+*   **Base Model:** TRELLIS.2-4B (sparse-structure / shape / texture VAEs and decoders)
+
+## Key Features
+
+*   **Structure-aware scene generation:** Recovers a coherent global layout from a single 360° image,
+    not just isolated assets.
+*   **Asset-aware output:** The scene is decomposed into a *layout* (floor and walls) and *individual
+    objects*, each exported as its own mesh rather than a single fused blob.
+*   **View-selective cross-attention:** The panorama is unwrapped into 6 cubemap faces (FOV 120°), and
+    each voxel attends only to the faces visible from its 3D position.
+*   **Layout-Guided Structure Inversion (optional):** A monocular-depth (Depth-Anything-2) point
+    cloud, the Partial Scene Geometry, seeds coarse generation via SDEdit-style noise inversion for
+    better room-scale fidelity.
+*   **PBR materials:** Base color, roughness, metallic, and opacity, inherited from the TRELLIS.2
+    texture decoder.
+
+## Checkpoints
+
+| Folder | Component | Role | Size |
+|--------|-----------|------|------|
+| `erp_ss_flow_img_dit_L_16l8_bf16_spatial/` | Coarse geometry | Coarse scene structure (sparse-structure flow, view-selective cross-attention) | ~4.9 GB |
+| `bbox_centerpoint/` | 3D BBox | Per-asset oriented bounding-box estimator (CenterPoint) | ~48 MB |
+| `erp_slat_flow_img2shape_asset_aware_bf16/` | Asset shape | Asset-aware shape generation | ~4.9 GB |
+| `erp_slat_flow_imgshape2tex_asset_aware_bf16/` | Asset texture | Asset-aware texture generation (PBR) | ~4.9 GB |
+
+Each folder holds the EMA weight under `ckpts/`. Model configs ship with the code repository
+(under `configs/`), so no `config.json` is needed here.
+
+## Requirements
+
+- **System:** Tested on **Linux**.
+- **Hardware:** An NVIDIA GPU with at least 24 GB of memory (verified on NVIDIA A100 and H100).
+- **Software:**
+  - The [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive) (recommended 12.4).
+  - [Conda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) for managing dependencies.
+  - Python 3.8 or higher.
+
+## Usage
+
+Please refer to the [official GitHub repository](https://github.com/kookie12/InSpace) for
+installation. InSpace is run through the repository's scripts (`demo/app_inspace_*.py`,
+`eval/pipeline/eval_pipeline.py`), which load these checkpoints and chain the multi-stage pipeline
+together.
+
+```sh
+# 1. Get the code and set up the environment (same env as TRELLIS.2)
+git clone https://github.com/kookie12/InSpace --recursive && cd InSpace
+. ./setup.sh --new-env --basic --flash-attn --nvdiffrast --nvdiffrec --cumesh --o-voxel --flexgemm
+
+# 2. Download the checkpoints into ckpts/ (repo mirrors the local layout)
+pip install -U "huggingface_hub[cli]"
+hf download GwanHyeong/InSpace --include "ckpts/*" --local-dir .
+
+# 3a. Interactive demo (pick a scene, run the pipeline stage by stage)
+python demo/app_inspace_erp_front.py --port 7860
+
+# 3b. Batch inference over the test set
+python eval/pipeline/eval_pipeline.py \
+    --data_dir datasets/ERP_3D_FRONT_test \
+    --noise_mode sdedit --sdedit_alpha 0.5 --bbox_mode predicted
+```
+
+The inference code loads each checkpoint from `ckpts/<folder>/ckpts/*.pt`; the matching model
+config is read from the code repository's `configs/` directory.
+
+## Dataset
+
+InSpace is trained on **[ERP-FRONT-30K](https://huggingface.co/datasets/GwanHyeong/ERP-FRONT-30K)**,
+a paired ERP-Image-to-3D indoor scene dataset built on 3D-FRONT, with **26.5K training** and **2.5K
+test** ERP-image-mesh pairs (~30K total). Each room is paired with 360° ERP observations rendered
+from inside the scene and covers a wide range of room sizes.
+
+```sh
+hf download GwanHyeong/ERP-FRONT-30K --repo-type dataset --local-dir datasets/
+```
+
+## Known Limitations
+
+*   **Domain of training data:** InSpace is trained on ERP-FRONT (synthetic 3D-FRONT scenes). Results
+    on real captured panoramas may vary; for real images the pipeline relies on monocular depth to build the Partial Scene Geometry.
+*   **Raw mesh artifacts:** As with TRELLIS.2, generated raw meshes may occasionally contain small
+    holes or minor topological discontinuities; mesh post-processing (hole-filling, remeshing) is
+    provided.
+
+## Citation
+
+InSpace has been accepted to ECCV 2026. If you find our work useful, please cite:
+
+```bibtex
+@article{koo2026inspace,
+  title   = {InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360{\deg} Image},
+  author  = {Koo, Gwanhyeong and Kim, Hyunsu and Kim, Youngji and Lee, Taejae and
+             Lim, Siwoo and Yoon, Sunjae and Yeon, Suyong and Yoo, Chang D.},
+  journal = {arXiv preprint arXiv:2607.03990},
+  year    = {2026}
+}
+```
+
+## License
+
+Released under the [MIT License](https://github.com/kookie12/InSpace/blob/main/LICENSE). This work
+builds on TRELLIS.2 (MIT, Microsoft). Some dependencies (e.g. nvdiffrast, nvdiffrec) carry their own
+licenses.

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