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Datasets:
Hgodwarrior
/
Huhb3D-Topology-Preview

Tasks:
Other
Modalities:
3D
Size:
n<1K
Tags:
robotics
3d
cad
topology
step
industrial
License:
Dataset card Files
xet
 Community
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
source_file: string
total_triangles: int64
total_faces: int64
shape_bounds: list<item: double>
  child 0, item: double
linear_deflection: double
angular_deflection: double
category_names: struct<0: string, 1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: st (... 76 chars omitted)
  child 0, 0: string
  child 1, 1: string
  child 2, 2: string
  child 3, 3: string
  child 4, 4: string
  child 5, 5: string
  child 6, 6: string
  child 7, 7: string
  child 8, 8: string
  child 9, 9: string
  child 10, 10: string
  child 11, 11: string
  child 12, 12: string
  child 13, 13: string
  child 14, 14: string
triangle_labels: list<item: int64>
  child 0, item: int64
faces: list<item: struct<face_id: int64, geom_type: string, category_id: int64, category_name: string, area (... 151 chars omitted)
  child 0, item: struct<face_id: int64, geom_type: string, category_id: int64, category_name: string, area: double, t (... 139 chars omitted)
      child 0, face_id: int64
      child 1, geom_type: string
      child 2, category_id: int64
      child 3, category_name: string
      child 4, area: double
      child 5, triangle_count: int64
      child 6, triangle_start: int64
      child 7, extra: struct<radius: double, axis_direction: list<item: double>, normal: list<item: double>>
          child 0, radius: double
          child 1, axis_direction: list<item: double>
              child 0, item: double
          child 2, normal: list<item: double>
              ch
...
ouble>
      child 0, face_count: int64
      child 1, total_area: double
  child 2, lateral_pinch: struct<face_count: int64, total_area: double>
      child 0, face_count: int64
      child 1, total_area: double
  child 3, expansion_grip: struct<face_count: int64, total_area: double>
      child 0, face_count: int64
      child 1, total_area: double
best_grasp_strategy: struct<method: string, gripper_type: string, target_face_id: int64, approach_direction: list<item: d (... 46 chars omitted)
  child 0, method: string
  child 1, gripper_type: string
  child 2, target_face_id: int64
  child 3, approach_direction: list<item: double>
      child 0, item: double
  child 4, confidence: double
  child 5, reasoning: string
face_recommendations: list<item: struct<face_id: int64, category_name: string, geom_type: string, area: double, grasp_meth (... 142 chars omitted)
  child 0, item: struct<face_id: int64, category_name: string, geom_type: string, area: double, grasp_method: string, (... 130 chars omitted)
      child 0, face_id: int64
      child 1, category_name: string
      child 2, geom_type: string
      child 3, area: double
      child 4, grasp_method: string
      child 5, gripper_type: string
      child 6, approach_direction: list<item: double>
          child 0, item: double
      child 7, grasp_point: list<item: double>
          child 0, item: double
      child 8, confidence: double
      child 9, notes: string
object_bounds: list<item: double>
  child 0, item: double
to
{'source_file': Value('string'), 'object_bounds': List(Value('float64')), 'best_grasp_strategy': {'method': Value('string'), 'gripper_type': Value('string'), 'target_face_id': Value('int64'), 'approach_direction': List(Value('float64')), 'confidence': Value('float64'), 'reasoning': Value('string')}, 'face_recommendations': List({'face_id': Value('int64'), 'category_name': Value('string'), 'geom_type': Value('string'), 'area': Value('float64'), 'grasp_method': Value('string'), 'gripper_type': Value('string'), 'approach_direction': List(Value('float64')), 'grasp_point': List(Value('float64')), 'confidence': Value('float64'), 'notes': Value('string')}), 'grasp_method_summary': {'contour_grip': {'face_count': Value('int64'), 'total_area': Value('float64')}, 'surface_grip': {'face_count': Value('int64'), 'total_area': Value('float64')}, 'lateral_pinch': {'face_count': Value('int64'), 'total_area': Value('float64')}, 'expansion_grip': {'face_count': Value('int64'), 'total_area': Value('float64')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              source_file: string
              total_triangles: int64
              total_faces: int64
              shape_bounds: list<item: double>
                child 0, item: double
              linear_deflection: double
              angular_deflection: double
              category_names: struct<0: string, 1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: st (... 76 chars omitted)
                child 0, 0: string
                child 1, 1: string
                child 2, 2: string
                child 3, 3: string
                child 4, 4: string
                child 5, 5: string
                child 6, 6: string
                child 7, 7: string
                child 8, 8: string
                child 9, 9: string
                child 10, 10: string
                child 11, 11: string
                child 12, 12: string
                child 13, 13: string
                child 14, 14: string
              triangle_labels: list<item: int64>
                child 0, item: int64
              faces: list<item: struct<face_id: int64, geom_type: string, category_id: int64, category_name: string, area (... 151 chars omitted)
                child 0, item: struct<face_id: int64, geom_type: string, category_id: int64, category_name: string, area: double, t (... 139 chars omitted)
                    child 0, face_id: int64
                    child 1, geom_type: string
                    child 2, category_id: int64
                    child 3, category_name: string
                    child 4, area: double
                    child 5, triangle_count: int64
                    child 6, triangle_start: int64
                    child 7, extra: struct<radius: double, axis_direction: list<item: double>, normal: list<item: double>>
                        child 0, radius: double
                        child 1, axis_direction: list<item: double>
                            child 0, item: double
                        child 2, normal: list<item: double>
                            ch
              ...
              ouble>
                    child 0, face_count: int64
                    child 1, total_area: double
                child 2, lateral_pinch: struct<face_count: int64, total_area: double>
                    child 0, face_count: int64
                    child 1, total_area: double
                child 3, expansion_grip: struct<face_count: int64, total_area: double>
                    child 0, face_count: int64
                    child 1, total_area: double
              best_grasp_strategy: struct<method: string, gripper_type: string, target_face_id: int64, approach_direction: list<item: d (... 46 chars omitted)
                child 0, method: string
                child 1, gripper_type: string
                child 2, target_face_id: int64
                child 3, approach_direction: list<item: double>
                    child 0, item: double
                child 4, confidence: double
                child 5, reasoning: string
              face_recommendations: list<item: struct<face_id: int64, category_name: string, geom_type: string, area: double, grasp_meth (... 142 chars omitted)
                child 0, item: struct<face_id: int64, category_name: string, geom_type: string, area: double, grasp_method: string, (... 130 chars omitted)
                    child 0, face_id: int64
                    child 1, category_name: string
                    child 2, geom_type: string
                    child 3, area: double
                    child 4, grasp_method: string
                    child 5, gripper_type: string
                    child 6, approach_direction: list<item: double>
                        child 0, item: double
                    child 7, grasp_point: list<item: double>
                        child 0, item: double
                    child 8, confidence: double
                    child 9, notes: string
              object_bounds: list<item: double>
                child 0, item: double
              to
              {'source_file': Value('string'), 'object_bounds': List(Value('float64')), 'best_grasp_strategy': {'method': Value('string'), 'gripper_type': Value('string'), 'target_face_id': Value('int64'), 'approach_direction': List(Value('float64')), 'confidence': Value('float64'), 'reasoning': Value('string')}, 'face_recommendations': List({'face_id': Value('int64'), 'category_name': Value('string'), 'geom_type': Value('string'), 'area': Value('float64'), 'grasp_method': Value('string'), 'gripper_type': Value('string'), 'approach_direction': List(Value('float64')), 'grasp_point': List(Value('float64')), 'confidence': Value('float64'), 'notes': Value('string')}), 'grasp_method_summary': {'contour_grip': {'face_count': Value('int64'), 'total_area': Value('float64')}, 'surface_grip': {'face_count': Value('int64'), 'total_area': Value('float64')}, 'lateral_pinch': {'face_count': Value('int64'), 'total_area': Value('float64')}, 'expansion_grip': {'face_count': Value('int64'), 'total_area': Value('float64')}}}
              because column names don't match

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Huhb3D Topology Preview Dataset

🆓 FREE PREVIEW — This dataset contains 2 objects as a teaser. The full version includes 20 industrial parts with 863 B-Rep faces and 46,168 mesh triangles. See Get Full Version below.

🚀 Get Full Version

Platform Link Price
🌍 Gumroad Huhb3D Topology Dataset From $29
🇨🇳 面包多 Huhb3D 拓扑数据集 From ¥99

The full version includes:

  • 20 industrial parts (flanges, gears, bearings, bolts, brackets, valves, etc.)
  • 863 B-Rep faces across 12 topology categories
  • 46,168 mesh triangles with per-triangle labels
  • STEP source files for all 20 objects
  • Grasp recommendations for all 20 objects
  • 4 themed subsets: Flange, Gear, Fastener & Bearing, Industrial All

What Makes This Dataset Unique

This is the only publicly available dataset that provides STEP-parsed per-face topology labels for industrial CAD models. Unlike mesh-only datasets, our topology annotations are derived directly from the STEP B-Rep structure using OpenCascade, providing:

  • Per-face semantic labels: Each mesh face is classified into one of 15 topology categories
  • STEP source files: Original parametric CAD models for precise geometry queries
  • Robotic grasp planning: Topology labels enable grasp strategy selection by face type
  • 6DoF pose estimation: Face-level annotations support pose refinement algorithms
  • 15 topology categories: From HorizontalPlane to SphericalSurface, covering all common industrial features

Overview

  • 2 industrial mechanical parts (preview)
  • 23 total B-Rep faces
  • 1794 total mesh triangles
  • 9 topology categories with per-face semantic labels
  • Source models: STEP (ISO 10303-21) CAD files

Directory Structure 

Huhb3D-Topology-Preview/
  README.md
  LICENSE
  dataset_info.json
  source_step/
    coupling.step
    hex_bolt.step
  objects/
    coupling/
      topology_labels.json       # Per-triangle topology labels
      topology_summary.json      # Topology statistics summary
      grasp_recommendations.json # Grasp recommendations
    hex_bolt/
      topology_labels.json
      topology_summary.json
      grasp_recommendations.json

Object List

Object Faces Triangles STEP Grasp Topology Categories
coupling 6 1160 ✓ ✓ Boss, ConcaveFeature_Hole, FreeSurface, LateralPlane_Z
hex_bolt 17 634 ✓ ✓ Boss, Chamfer, HorizontalPlane, LateralPlane_Z, NearHorizontal, NearLateral_X, NearLateral_Z

Topology Categories

ID Category Description Color
0 FreeSurface 自由曲面（圆柱面、圆锥面、B样条曲面等） #7F7F7F
1 HorizontalPlane 法线平行于 Z 轴的平面（顶面/底面） #0000FF
3 LateralPlane_Z 法线平行于 Z 轴的竖直平面 #FF0000
4 NearHorizontal 与水平面倾斜角 <30° 的平面 #FFFF00
5 NearLateral_X 与 X 侧面倾斜角 <30° 的平面 #FF00FF
6 NearLateral_Z 与 Z 侧面倾斜角 <30° 的平面 #00FFFF
9 ConcaveFeature_Hole 凹陷圆柱特征（孔、内腔、凹槽） #007FFF
11 Boss 凸起圆柱台（安装凸台、垫台） #00CC66
12 Chamfer 两个面之间的倾斜过渡边 #CC6600

💡 The full version covers all 15 categories across 20 objects. This preview shows 9 of them.

Grasp Recommendations

Each object includes grasp_recommendations.json with pre-computed robotic grasp poses. These files contain:

  • Recommended grasp approach directions
  • Gripper type suggestions (vacuum, parallel jaw, soft gripper, etc.)
  • Grasp quality scores (confidence)
  • Best grasp strategy with reasoning

Data Format

topology_labels.json

Per-object file containing:

  • source_file: Original STEP file name
  • total_triangles: Total number of mesh triangles
  • total_faces: Total number of B-Rep faces
  • shape_bounds: Bounding box [xmin, ymin, zmin, xmax, ymax, zmax]
  • category_names: Mapping from category ID to name
  • triangle_labels: Array of category IDs, one per triangle
  • faces: Array of face objects with:
    • face_id, geom_type, category_id, category_name
    • area, triangle_count, triangle_start
    • extra: Optional dict with radius, axis_direction, normal

topology_summary.json

Per-object file containing:

  • source_file, total_faces, total_triangles, shape_bounds
  • categories: Dict mapping category ID to name, face_count, triangle_count, total_area

grasp_recommendations.json

Per-object file containing:

  • source_file, object_bounds
  • best_grasp_strategy: Overall best grasp method with confidence and reasoning
  • face_recommendations: Per-face grasp suggestions with approach directions and gripper types
  • grasp_method_summary: Aggregated statistics by grasp method

Quick Start 

import json
from pathlib import Path

# 加载拓扑摘要
with open("objects/coupling/topology_summary.json") as f:
    summary = json.load(f)
print(f"Faces: {summary['total_faces']}, Triangles: {summary['total_triangles']}")

# 加载抓取推荐
with open("objects/coupling/grasp_recommendations.json") as f:
    grasp = json.load(f)
print(f"Best strategy: {grasp['best_grasp_strategy']['method']}")
print(f"Confidence: {grasp['best_grasp_strategy']['confidence']}")

Citation

If you use this dataset in your research, please cite:

@dataset{huhb3d_topology_preview,
  title   = {Huhb3D Topology Preview Dataset},
  author  = {Huhb},
  year    = {2026},
  version = {1.0.0},
  url     = {https://github.com/huhb-ai/Huhb3D-Topology-Dataset}
}

License

  • 3D Models: CC0 (Public Domain) — original creations, no restrictions
  • Dataset (annotations, metadata, packaging): CC-BY-4.0 — attribution required

You are free to share and adapt for any purpose, including commercially, as long as appropriate credit is given.

See LICENSE for the full license text.

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