| --- |
| license: apache-2.0 |
| tags: |
| - robotics |
| - simulation |
| - 3d-assets |
| - articulated-objects |
| - drake |
| - sdf |
| pretty_name: SceneSmith Preprocessed Data |
| --- |
| |
| # SceneSmith Preprocessed Data |
|
|
| Preprocessed 3D assets for use with [SceneSmith](https://github.com/nepfaff/scenesmith), a VLM-agent-based system for generating physically realistic, interactive indoor scenes. |
|
|
| ## ArtVIP (Articulated Objects) |
|
|
| Simulation-ready articulated objects (cabinets, drawers, appliances, etc.) converted from the [ArtVIP](https://huggingface.co/datasets/x-humanoid-robomind/ArtVIP) dataset. Assets have been converted from USD to Drake SDFormat using [mesh-to-sim-asset](https://github.com/nepfaff/mesh-to-sim-asset), with: |
|
|
| - **Drake SDFormat (.sdf)** model files with articulated joints |
| - **Visual meshes** in glTF and OBJ formats |
| - **Collision geometries** via convex decomposition (two variants available) |
| - **Estimated physical properties** (mass, inertia per link) |
| - **Pre-computed CLIP embeddings** for text-based retrieval |
|
|
| ### Variants |
|
|
| | Archive | Collision Method | Description | |
| |---------|-----------------|-------------| |
| | `artvip/artvip_vhacd.tar.gz` | VHACD | Tighter collision geometries (recommended) | |
| | `artvip/artvip_coacd.tar.gz` | CoACD | Can produce faster simulations | |
|
|
| Both variants include identical CLIP embeddings for retrieval. |
|
|
| ### Usage with SceneSmith |
|
|
| Download your preferred variant and extract to `data/artvip_sdf/`: |
|
|
| ```sh |
| # Download VHACD variant (recommended) |
| huggingface-cli download nepfaff/scenesmith-preprocessed-data \ |
| artvip/artvip_vhacd.tar.gz --repo-type dataset --local-dir . |
| mkdir -p data/artvip_sdf |
| tar xzf artvip/artvip_vhacd.tar.gz -C data/artvip_sdf |
| rm -rf artvip |
| |
| # Or download CoACD variant |
| huggingface-cli download nepfaff/scenesmith-preprocessed-data \ |
| artvip/artvip_coacd.tar.gz --repo-type dataset --local-dir . |
| mkdir -p data/artvip_sdf |
| tar xzf artvip/artvip_coacd.tar.gz -C data/artvip_sdf |
| rm -rf artvip |
| ``` |
|
|
| ### Asset Categories |
|
|
| | Category | Description | |
| |----------|-------------| |
| | `large_furniture/` | Cupboards, wardrobes, etc. | |
| | `small_furniture/` | Nightstands, side tables, etc. | |
| | `major_appliances/` | Refrigerators, washing machines, etc. | |
| | `small_appliances/` | Microwaves, toasters, etc. | |
| | `household_items/` | Storage boxes, bins, etc. | |
|
|
| ### Manual Preprocessing |
|
|
| To preprocess ArtVIP assets yourself (or to process updated versions), use [mesh-to-sim-asset](https://github.com/nepfaff/mesh-to-sim-asset) to convert from USD to Drake SDFormat. |
|
|
| ## AmbientCG Material Embeddings |
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|
| Pre-computed CLIP embeddings for [AmbientCG](https://ambientcg.com/) PBR materials, enabling text-based material retrieval for walls, floors, and surfaces. |
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|
| The embeddings are computed from AmbientCG preview images using CLIP (ViT-H-14-378-quickgelu, 1024D). |
|
|
| > **Note:** You still need to download the actual material textures from AmbientCG separately (see SceneSmith README). This dataset only contains the pre-computed embeddings to skip the computation step. |
|
|
| ### Usage with SceneSmith |
|
|
| ```sh |
| huggingface-cli download nepfaff/scenesmith-preprocessed-data \ |
| --repo-type dataset \ |
| --include "ambientcg/embeddings/**" \ |
| --local-dir data/scenesmith-preprocessed-data |
| mv data/scenesmith-preprocessed-data/ambientcg/embeddings data/materials/embeddings |
| rm -rf data/scenesmith-preprocessed-data |
| ``` |
|
|
| ## Attribution |
|
|
| The articulated object assets are derived from the [ArtVIP](https://huggingface.co/datasets/x-humanoid-robomind/ArtVIP) dataset, licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
|
|
| > Zhao Jin, Zhengping Che, Tao Li, Zhen Zhao, Kun Wu, Yuheng Zhang, Yinuo Zhao, Zehui Liu, Qiang Zhang, Xiaozhu Ju, Jing Tian, Yousong Xue, Jian Tang. *ArtVIP: Articulated Digital Assets of Visual Realism, Modular Interaction, and Physical Fidelity for Robot Learning.* 2025. |
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