metadata
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
dtype: binary
- name: glb
dtype: binary
- name: singleview_image
dtype: image
- name: multiview_image
dtype: image
- name: pbr
dtype: image
- name: noisy_stl
dtype: binary
splits:
- name: bench0
num_bytes: 8687768922
num_examples: 3000
- name: bench0F
num_bytes: 9820522555
num_examples: 3000
- name: bench1A
num_bytes: 14881149982
num_examples: 3000
- name: bench1B
num_bytes: 15182316852
num_examples: 3000
- name: bench2
num_bytes: 9795102660
num_examples: 3000
- name: bench3
num_bytes: 43616021783
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.
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.