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bardofcodes
/
migumi-dataset

Languages:
English
Size:
n<1K
Tags:
geometry
fabrication
symbolic-geometry
cnc
sdf
License:
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
moduleList: struct<base: struct<nodes: list<item: struct<id: string, name: string, data: struct<size: list<item: double>, name: string, bbox: list<item: int64>, origin: list<item: double>, normal: list<item: int64>, material: list<item: int64>, height: list<item: double>, state: list<item: int64>, points: list<item: list<item: double>>, param: list<item: double>>>>, connections: list<item: struct<source: string, sourceOutput: string, target: string, targetInput: string>>, positions: struct<759756b0a93c59e4: struct<x: int64, y: int64>, 8c4f761aa4adc8cd: struct<x: int64, y: int64>, ddb014729ec7c645: struct<x: int64, y: int64>, 5a2a512ce9d0614a: struct<x: int64, y: int64>, 5e6136a4d9d16b05: struct<x: int64, y: int64>, 3dc68f536f28b973: struct<x: int64, y: int64>, d14349ae44ca9bf3: struct<x: int64, y: int64>, 3046bcac773e9e49: struct<x: int64, y: int64>, a998afee1d931c86: struct<x: int64, y: int64>, dc98d2e4cb2063c7: struct<x: int64, y: int64>, f971e5040ed817d3: struct<x: int64, y: int64>, d1743d26218bb56d: struct<x: int64, y: int64>, 105a6498b1500f6f: struct<x: int64, y: int64>, 81f9237304b7dd10: struct<x: int64, y: int64>, 2ac9881ecebe0fc8: struct<x: int64, y: int64>, 0a1b015b8ced6c5d: struct<x: int64, y: int64>, 33b3983c902c8b4f: struct<x: int64, y: int64>, 18aac148cc36bd52: struct<x: int64, y: int64>, 4e058a75cb82cce9: struct<x: int64, y: int64>, 11a9ac9efa98529c: struct<x: int64, y: int64>, ae8a1721a28b7bb6: struct<x: int64, y: int64>, a750c64bb6b3302c: struct<x: int64, y: int64>, 88967692f7e9a59c: struct<x: int64, y: int64>, aa111a99fc335242: struct<x: int64, y: int64>, bfba3fd93a2f2ff8: struct<x: int64, y: int64>, f6ec23e234abc7b9: struct<x: int64, y: int64>, 6b8b6e68e6d7a82c: struct<x: int64, y: int64>, d92b603f07d32a9d: struct<x: int64, y: int64>, a5af0799e0eb6f50: struct<x: int64, y: int64>, 2ea9d710d0d00b8b: struct<x: int64, y: int64>, 7e0def2c6c3d046f: struct<x: int64, y: int64>, 6095b042e7e5063c: struct<x: int64, y: int64>, 3fc1e195929f9e0f: struct<x: int64, y: int64>, 96f182d19fd0851f: struct<x: int64, y: int64>, b92b2b1b77397aa5: struct<x: int64, y: int64>, 5b7890c1f669d09f: struct<x: int64, y: int64>, dacfc2dd067f27fc: struct<x: int64, y: int64>, 4236e9e53bbed053: struct<x: int64, y: int64>, abda5128e03c5a6c: struct<x: int64, y: int64>, 34a9619ebbbc3a1f: struct<x: int64, y: int64>, a06e65db48254692: struct<x: int64, y: int64>, 31c042b794712b0d: struct<x: int64, y: int64>, 229813bffd215743: struct<x: int64, y: int64>, 9747188886efe5e5: struct<x: int64, y: int64>, e6bb809932132579: struct<x: int64, y: int64>, 5cedeab1575de5c1: struct<x: int64, y: int64>, 3adf7779d95ee1f8: struct<x: int64, y: int64>, 482d8a96c8d46743: struct<x: int64, y: int64>, 168d60aac83f43c2: struct<x: int64, y: int64>, 2bcc57cdbc9627fe: struct<x: int64, y: int64>, 132ea50dd3b9bf08: struct<x: int64, y: int64>, ba1187118d5dda67: struct<x: int64, y: int64>, b71a6770343e1041: struct<x: int64, y: int64>, 96d36df6297a1761: struct<x: int64, y: int64>>>>
vs
name: string
type: string
n_parts: int64
assembly_steps: int64
notes: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 531, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              moduleList: struct<base: struct<nodes: list<item: struct<id: string, name: string, data: struct<size: list<item: double>, name: string, bbox: list<item: int64>, origin: list<item: double>, normal: list<item: int64>, material: list<item: int64>, height: list<item: double>, state: list<item: int64>, points: list<item: list<item: double>>, param: list<item: double>>>>, connections: list<item: struct<source: string, sourceOutput: string, target: string, targetInput: string>>, positions: struct<759756b0a93c59e4: struct<x: int64, y: int64>, 8c4f761aa4adc8cd: struct<x: int64, y: int64>, ddb014729ec7c645: struct<x: int64, y: int64>, 5a2a512ce9d0614a: struct<x: int64, y: int64>, 5e6136a4d9d16b05: struct<x: int64, y: int64>, 3dc68f536f28b973: struct<x: int64, y: int64>, d14349ae44ca9bf3: struct<x: int64, y: int64>, 3046bcac773e9e49: struct<x: int64, y: int64>, a998afee1d931c86: struct<x: int64, y: int64>, dc98d2e4cb2063c7: struct<x: int64, y: int64>, f971e5040ed817d3: struct<x: int64, y: int64>, d1743d26218bb56d: struct<x: int64, y: int64>, 105a6498b1500f6f: struct<x: int64, y: int64>, 81f9237304b7dd10: struct<x: int64, y: int64>, 2ac9881ecebe0fc8: struct<x: int64, y: int64>, 0a1b015b8ced6c5d: struct<x: int64, y: int64>, 33b3983c902c8b4f: struct<x: int64, y: int64>, 18aac148cc36bd52: struct<x: int64, y: int64>, 4e058a75cb82cce9: struct<x: int64, y: int64>, 11a9ac9efa98529c: struct<x: int64, y: int64>, ae8a1721a28b7bb6: struct<x: int64, y: int64>, a750c64bb6b3302c: struct<x: int64, y: int64>, 88967692f7e9a59c: struct<x: int64, y: int64>, aa111a99fc335242: struct<x: int64, y: int64>, bfba3fd93a2f2ff8: struct<x: int64, y: int64>, f6ec23e234abc7b9: struct<x: int64, y: int64>, 6b8b6e68e6d7a82c: struct<x: int64, y: int64>, d92b603f07d32a9d: struct<x: int64, y: int64>, a5af0799e0eb6f50: struct<x: int64, y: int64>, 2ea9d710d0d00b8b: struct<x: int64, y: int64>, 7e0def2c6c3d046f: struct<x: int64, y: int64>, 6095b042e7e5063c: struct<x: int64, y: int64>, 3fc1e195929f9e0f: struct<x: int64, y: int64>, 96f182d19fd0851f: struct<x: int64, y: int64>, b92b2b1b77397aa5: struct<x: int64, y: int64>, 5b7890c1f669d09f: struct<x: int64, y: int64>, dacfc2dd067f27fc: struct<x: int64, y: int64>, 4236e9e53bbed053: struct<x: int64, y: int64>, abda5128e03c5a6c: struct<x: int64, y: int64>, 34a9619ebbbc3a1f: struct<x: int64, y: int64>, a06e65db48254692: struct<x: int64, y: int64>, 31c042b794712b0d: struct<x: int64, y: int64>, 229813bffd215743: struct<x: int64, y: int64>, 9747188886efe5e5: struct<x: int64, y: int64>, e6bb809932132579: struct<x: int64, y: int64>, 5cedeab1575de5c1: struct<x: int64, y: int64>, 3adf7779d95ee1f8: struct<x: int64, y: int64>, 482d8a96c8d46743: struct<x: int64, y: int64>, 168d60aac83f43c2: struct<x: int64, y: int64>, 2bcc57cdbc9627fe: struct<x: int64, y: int64>, 132ea50dd3b9bf08: struct<x: int64, y: int64>, ba1187118d5dda67: struct<x: int64, y: int64>, b71a6770343e1041: struct<x: int64, y: int64>, 96d36df6297a1761: struct<x: int64, y: int64>>>>
              vs
              name: string
              type: string
              n_parts: int64
              assembly_steps: int64
              notes: string

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MiGumi Dataset: Dataset of Millable Kigumi Joints

assets/teaser.png

This dataset accompanies the paper "MiGumi: Making Tightly Coupled Integral Joints Millable" (SIGGRAPH Asia 2025)
and provides symbolic, geometric, and mesh representations of traditional Japanese joints as well as their redesigned variants for CNC milling.

Dataset Structure

All the joints are stored in joints folder. Each joint folder (e.g., CJ_AKT/) contains:

base.json                 # The manually created input Joint Design in MXG language.
info.json                 # High-level metadata for the joint
polyline_files/           # MXG representation for the different variants
vis_files/                # Conversion into a export friendly format (used with blender)
meshes/                   # STL meshes for each variant created using Trimesh & Shapely

Variants include:

  • base: original joint geometry

  • mill: Milling Only baseline: Applies morphological opening to each part’s extruded region, producing millable geometry by construction. No attempt is made to preserve surface coupling.

  • odf: Opening & Diff-Flip: Begins with millable parts obtained via opening, then restores contact by applying the resulting shape differences to paired parts. That is, the volume of each part removed due to the opening operation (Diff) is directly added to the paired part (Flip). Although simple, this heuristic often yields invalid subtractions that break millability.

  • ours: Our Method which optimizes the joint designs with losses that model tight coupling between joints along with milling.

Generally, ODF results in cleaner designs with smoother milling contours. However, there are two issues:

  • It fails for various designs (check out CJ_SAT, CJ_KMKT etc.).

  • It sometimes contains thin tiny slivers in the outline due to issues with the polycurve boolean operations.

In contrast, our approach provides a feasible solution for all the joint designs considered above.

Usage

You can clone the dataset directly:

git lfs install
git clone https://huggingface.co/datasets/bardofcodes/migumi-dataset

Or load it with the Hugging Face Datasets API:

from datasets import load_dataset
ds = load_dataset("bardofcodes/MiGumi-Dataset")

(If the dataset is structured by folders, the library will list joint names and file paths.)

Dataset Overview

assets/full_dataset

License

This dataset is released under the MIT License.

Citation

If you use this dataset, please cite:

@dataset{ganeshan2025_migumi_dataset,
  author    = {Ganeshan, Aditya and Fleischer, Kurt and Jakob, Wenzel
                and Shamir, Ariel and Ritchie, Daniel 
                and Igarashi, Takeo and Larsson, Maria},
  title     = {MiGumi Dataset: Millable Kigumi Joints Dataset},
  year      = {2025},
  note      = {Available at https://huggingface.co/datasets/bardofcodes/MiGumi-Dataset}
}

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