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task_categories:
- image-to-text
dataset_info:
features:
- name: file_id
dtype: string
- name: label
dtype: string
- name: step
dtype: binary
- name: stl
dtype: binary
- name: obj
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- name: glb
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- name: singleview_image
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- name: noisy_stl
dtype: binary
splits:
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num_examples: 3000
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num_examples: 3000
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- name: bench2
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num_examples: 3000
- name: bench3
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num_examples: 3000
download_size: 80229154806
dataset_size: 103734295182
configs:
- config_name: default
data_files:
- split: bench0
path: data/bench0-*
- split: bench1A
path: data/bench1A-*
- split: bench1B
path: data/bench1B-*
- split: bench2
path: data/bench2-*
- split: bench3
path: data/bench3-*
- split: bench0F
path: data/bench0F-*
---
# CADBench
CADBench is a unified multimodal benchmark for AI-assisted CAD program generation, introduced in the paper [CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation](https://huggingface.co/papers/2605.10873).
The benchmark contains 18,000 evaluation samples spanning six benchmark families derived from DeepCAD, Fusion 360, ABC, MCB, and Objaverse. It is designed to measure progress in editable 3D reconstruction and multimodal CAD understanding.
### Dataset Summary
CADBench supports evaluation across five input modalities:
- **Clean meshes**
- **Noisy meshes**
- **Single-view renders**
- **Photorealistic renders (PBR)**
- **Multi-view renders**
The benchmark evaluates models across six metrics covering geometric fidelity, executability, and program compactness. STEP-based families are stratified by B-rep face count to support controlled analysis across complexity and object variation. |