| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| license_link: LICENSE |
| task_categories: |
| - other |
| tags: |
| - 3d |
| - shape |
| - primitives |
| - superfrustum |
| - superfit |
| - pickle |
| - point-cloud |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: toys4k_cuboid |
| data_files: |
| - split: train |
| path: manifest.jsonl |
| default: false |
| - config_name: toys4k_superfrustum |
| data_files: |
| - split: train |
| path: manifest.jsonl |
| default: true |
| - config_name: partobjaverse_superfrustum |
| data_files: |
| - split: train |
| path: manifest.jsonl |
| default: false |
| --- |
| |
| # SuperFit Primitive Assembly Release |
|
|
| Pre-computed **primitive assemblies** produced by [SuperFit](https://github.com/BardOfCodes/superfit) (CVPR 2026) on two public 3D shape benchmarks. Each instance stores the fitted primitive parameters, optimization statistics, and optional per-instance evaluation metrics as serialized Python pickles alongside the hyperparameter `config.json` used for fitting. |
|
|
| The manifest files can be inspected with standard-library Python only. Loading primitive-assembly pickles, recovering expressions, or generating meshes requires the [SuperFit codebase](https://github.com/BardOfCodes/superfit) and its runtime dependencies. |
|
|
| > **Intended use:** The dataset materials in this release are provided **for non-commercial research use only** under [CC BY-NC 4.0](LICENSE). The small release helper scripts are MIT-licensed; see [LICENSE](LICENSE) for scope. Source 3D meshes are **not** redistributed here; you must obtain [Toys4K](https://rehg.org/publication/dataset2/) and [PartObjaverse](https://partobjaverse.github.io/) / [Objaverse](https://objaverse.allenai.org/) under their respective terms before comparing to ground-truth geometry. Commercial use of this Dataset, in whole or in part, requires prior written permission from the authors. |
|
|
| ## Dataset structure |
|
|
| ``` |
| sf_release/ |
| ├── manifest.jsonl # one row per (subset, method, object) |
| ├── metadata.json # counts and schema summary |
| ├── load_release.py # stdlib manifest index + pickle helper |
| ├── scripts/ # build, sanitize, validate (maintainers) |
| ├── examples/ # usage examples |
| └── dataset/ # primitive-assembly artifacts |
| ├── toys4k/ |
| │ ├── cuboid/ # 500 instances + eval_summary_* |
| │ ├── sf_cvpr/ # 500 instances + eval_summary_* |
| │ ├── sp_proto/ # 500 instances |
| │ ├── superfrustum/ # 4000 instances + eval_summary_* |
| │ ├── supergeon/ # 500 instances |
| │ └── superquadric/ # 500 instances |
| └── partobjaverse/ |
| └── superfrustum/ # 200 instances + eval_summary_* |
| ``` |
|
|
| ### Subsets and methods |
|
|
| | Subset | Method folder | Primitive type (`PRIM_TYPE`) | Instances | |
| |--------|---------------|------------------------------|-----------| |
| | `toys4k` | `superfrustum` | `VarAxisSF` (SuperFrustum) | 4000 | |
| | `toys4k` | `cuboid` | `Cuboid` | 500 | |
| | `toys4k` | `sf_cvpr` | `SuperFrustum` | 500 | |
| | `toys4k` | `sp_proto` | `VarAxisSPP` | 500 | |
| | `toys4k` | `supergeon` | `VarAxisSG` | 500 | |
| | `toys4k` | `superquadric` | `VarAxisSQ` | 500 | |
| | `partobjaverse` | `superfrustum` | `VarAxisSF` | 200 | |
|
|
| Toys4K instance directories are named `{category}_{id}` (e.g. `airplane_003`). PartObjaverse instances use the 32-character Objaverse UID as the directory name. |
|
|
| ### Per-instance files |
|
|
| | File | Required | Description | |
| |------|----------|-------------| |
| | `config.json` | yes | Fit hyperparameters (`AlgorithmConfig`); `AOT_ARTIFACT_FILE` is nulled in the release | |
| | `primitive_assembly.pkl` | usually | Flat dict of optimization / assembly statistics from SuperFit (`Stats.get_dict()`) | |
| | `primitive_assembly_eval.pkl` | optional | Per-instance evaluation metrics | |
| | `primitive_assembly_error.pkl` | optional | Partial stats from runs where fitting failed (present in lieu of `primitive_assembly.pkl` for ~45 instances) | |
| | `primitive_assembly.pkl_textured.pkl` | optional | Textured assembly (`partobjaverse` only) | |
|
|
| Every instance directory contains **either** `primitive_assembly.pkl` (success) **or** `primitive_assembly_error.pkl` (failure). Filter on `has_primitive_assembly` in the manifest if you only want successful fits. |
|
|
| Method-level aggregates (where present): |
|
|
| - `eval_summary_start0_end500.md` / `.pkl` — mean metrics over Toys4K eval splits |
| - `eval_summary_start0_end200.md` / `.pkl` — mean metrics over PartObjaverse (200 objects) |
|
|
| ### Pickle schema |
|
|
| `primitive_assembly.pkl` is a **flat Python `dict`** produced by SuperFit's statistics manager. Keys are dot-separated paths (e.g. `iter_0.pruned_program`, `evaluation.iou@128`, `input_mesh_file`, timing scopes). Values are floats, strings, NumPy arrays, PyTorch tensors, nested dicts, or serialized SuperFit / GeoLIPI expression metadata. **Loading primitive assemblies requires Python 3.10+ and usually the SuperFit runtime**, including PyTorch / NumPy and the expression classes used by the saved programs. |
|
|
| See the [SuperFit repository](https://github.com/BardOfCodes/superfit) for the supported runtime and code that consumes these artifacts. |
|
|
| ### Pickle safety |
|
|
| Pickle files can execute arbitrary code during `pickle.load`. **Only load artifacts from this official dataset repository** (or your own trusted mirror). Do not unpickle files from untrusted third parties. |
|
|
| ## Usage |
|
|
| ### Clone and enable Git LFS |
|
|
| Binary artifacts are stored with **Git LFS**. After cloning: |
|
|
| ```bash |
| git lfs install |
| git clone https://huggingface.co/datasets/<org>/superfit-primitive-assemblies |
| cd superfit-primitive-assemblies |
| git lfs pull |
| ``` |
|
|
| ### Manifest discovery (stdlib only) |
|
|
| ```python |
| from pathlib import Path |
| from load_release import ReleaseIndex, load_metadata |
| |
| root = Path(".") |
| print(load_metadata(root)["total_instances"]) # 6700 |
| |
| index = ReleaseIndex(root) |
| row = index.get("toys4k", "superfrustum", "airplane_002") |
| print(row["primitive_assembly_path"]) |
| ``` |
|
|
| ### Loading artifacts (requires SuperFit runtime) |
|
|
| Primitive assemblies are Python pickles. Unpickling them can require SuperFit, |
| PyTorch, NumPy, and expression dependencies from the |
| [SuperFit repository](https://github.com/BardOfCodes/superfit): |
|
|
| ```python |
| from pathlib import Path |
| from load_release import ReleaseIndex |
| |
| root = Path(".") |
| index = ReleaseIndex(root) |
| row = index.get("toys4k", "superfrustum", "airplane_002") |
| assembly = index.load(row) # trusted pickle; requires SuperFit/PyTorch stack |
| print(type(assembly), list(assembly.keys())[:5]) |
| ``` |
|
|
| Command-line example: |
|
|
| ```bash |
| python examples/load_artifact.py --source toys4k --method cuboid --object-id airplane_003 |
| python examples/load_artifact.py --source partobjaverse --method superfrustum --artifact eval |
| ``` |
|
|
| ### Export a mesh with SuperFit |
|
|
| The released files store primitive expressions, not pre-meshed OBJ/GLB files. |
| To generate a mesh, install or add [SuperFit](https://github.com/BardOfCodes/superfit) |
| to your Python environment, load a saved expression, evaluate it as an SDF, and |
| use SuperFit's mesh extraction helper: |
|
|
| ```python |
| from pathlib import Path |
| |
| from geolipi.torch_compute import Sketcher, recursive_evaluate |
| from load_release import ReleaseIndex |
| from superfit.symbolic.utils import fetch_singular_expr_eval |
| from superfit.utils.io import get_best_expr |
| from superfit.utils.mesh_sdf import sdf_to_mesh |
| |
| root = Path(".") |
| index = ReleaseIndex(root) |
| row = index.get("toys4k", "superfrustum", "airplane_002") |
| info = index.load(row) # trusted pickle; requires SuperFit runtime |
| |
| expr = get_best_expr(info, prog_type="pruned_program") |
| expr = fetch_singular_expr_eval( |
| expr.tensor(device="cuda"), |
| temperature=10000.0, |
| relaxed_eval=True, |
| remove_marker=True, |
| device="cuda", |
| ) |
| |
| sketcher = Sketcher(resolution=128, n_dims=3, device="cuda") |
| sdf = recursive_evaluate(expr.tensor(device="cuda"), sketcher) |
| mesh = sdf_to_mesh(sdf, sketcher) |
| mesh.export("airplane_002.obj") |
| ``` |
|
|
| The same flow is available as: |
|
|
| ```bash |
| python examples/export_mesh_with_superfit.py \ |
| --source toys4k \ |
| --method superfrustum \ |
| --object-id airplane_002 \ |
| --output airplane_002.obj |
| ``` |
|
|
| SuperFit's current mesh extraction path uses PyTorch/Kaolin/FlexiCubes and is |
| normally run on CUDA. |
|
|
| ### Rebuild manifest / validate before upload |
|
|
| Maintainers can regenerate indexes and run checks from the repo root: |
|
|
| ```bash |
| python scripts/sanitize_configs.py # null local AOT paths (idempotent) |
| python scripts/build_manifest.py # writes manifest.jsonl + metadata.json |
| python scripts/build_manifest.py --checksums # optional sha256 columns |
| python scripts/validate_release.py |
| ``` |
|
|
| ## Limitations |
|
|
| - **Not a Parquet/Arrow dataset:** the Hugging Face Dataset Viewer will not render pickle contents; use `manifest.jsonl` for discovery. |
| - **No source meshes:** only fitted assemblies and configs are provided. |
| - **Incomplete eval coverage:** `dataset/toys4k/superfrustum` has per-instance eval pickles on a subset of instances; see `has_primitive_assembly_eval` in the manifest. |
| - **Environment coupling:** manifest discovery is stdlib-only, but unpickling primitive assemblies, recovering expressions, and exporting meshes require the SuperFit stack. |
|
|
| ## Licensing and provenance |
|
|
| | Component | License / terms | |
| |-----------|-----------------| |
| | **Dataset materials** (assemblies, configs, eval summaries, manifests, metadata, docs) | [CC BY-NC 4.0](LICENSE) - non-commercial research use only | |
| | **Release helper code** (`load_release.py`, `examples/*.py`, `scripts/*.py`) | [MIT](LICENSE) | |
| | **SuperFit codebase** | Distributed separately under its own license; see [SuperFit](https://github.com/BardOfCodes/superfit) | |
| | **Toys4K** source meshes | Request access via the [authors' form](https://forms.gle/w7Zf82umwaKxr9L7A); follow their terms | |
| | **PartObjaverse** (`dataset/partobjaverse/`) | Derived from [SAMPart3D](https://arxiv.org/abs/2411.07184) / Objaverse assets; cite Yang et al. (2024) below | |
| | **Objaverse** source meshes | Per-object licenses (CC-BY, CC-BY-NC, etc.); see [Objaverse](https://objaverse.allenai.org/) | |
|
|
| We recommend publishing this Hugging Face repo as **gated** until you confirm redistribution of derived fits is compatible with your Toys4K and Objaverse agreements. |
|
|
| This dataset release is **not** licensed under the Adobe Research License; that license applies to the separate SuperFit codebase. Details on source datasets and release posture: [PROVENANCE.md](PROVENANCE.md). |
|
|
| ## Citation |
|
|
| If you use this release, please cite SuperFit and the source datasets: |
|
|
| ```bibtex |
| @misc{ganeshan2026superfit, |
| title = {Residual Primitive Fitting of 3D Shapes with SuperFrusta}, |
| author = {Aditya Ganeshan and Matheus Gadelha and Thibault Groueix and Zhiqin Chen and Siddhartha Chaudhuri and Vladimir G. Kim and Wang Yifan and Daniel Ritchie}, |
| year = {2026}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| month = {June}, |
| } |
| ``` |
|
|
| ```bibtex |
| @inproceedings{stojanov2021toys4k, |
| title = {Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias}, |
| author = {Stefan Stojanov and Anh Thai and James M. Rehg}, |
| booktitle = {CVPR}, |
| year = {2021}, |
| } |
| ``` |
|
|
| ```bibtex |
| @article{yang2024sampart3d, |
| author = {Yang, Yunhan and Huang, Yukun and Guo, Yuan-Chen and Lu, Liangjun and Wu, Xiaoyang and Lam, Edmund Y. and Cao, Yan-Pei and Liu, Xihui}, |
| title = {SAMPart3D: Segment Any Part in 3D Objects}, |
| journal = {arXiv preprint arXiv:2411.07184}, |
| year = {2024}, |
| } |
| ``` |
|
|
| ```bibtex |
| @article{deitke2023objaverse, |
| title = {Objaverse: A Universe of Annotated 3D Objects}, |
| author = {Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs and others}, |
| journal = {arXiv:2212.08051}, |
| year = {2023}, |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Questions: `adityaganeshan@gmail.com` · [Project page](https://bardofcodes.github.io/superfit) |
|
|
