--- 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 **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//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)