license: cc-by-4.0
task_categories:
- image-to-3d
- text-to-3d
language:
- en
tags:
- 3d
- benchmark
- hard-surface
- cad
- shape-as-code
- reconstruction
pretty_name: MechBench-36
size_categories:
- n<1K
MechBench-36
36 hard-surface, single-object evaluation cases for image- and text-conditioned 3D generation.
Each case is one mechanically complex object — vehicles, robots, instruments, aerospace mechanisms, sci-fi hardware — rendered as a clean studio product shot, together with the text prompts used to condition and to evaluate generation. The suite was built to stress exactly what hard-surface objects punish: open frames, thin supports, coaxial nesting, articulated linkages, repeated and mirrored parts, deliberate asymmetry.
MechBench-36 is the evaluation suite of the paper Procedura: A Procedural Modeling Agent for 3D Shape-as-Code Generation (paper page).
Fields
| field | type | description |
|---|---|---|
id |
string | case identifier (e.g. assault_buggy) |
image |
image | the reference render, 1254×1254 RGB, white/light studio background, three-quarter product view |
condition_prompt |
string | short conditioning prompt describing the object — what an image- or text-conditioned method receives |
spec |
string | the full evaluation spec: the condition prompt plus what is (and is not) gradeable from the single reference view |
reference_generation_prompt |
string | the prompt used to generate the reference image itself (provenance / reproducibility) |
track |
string | benchmark track (object) |
difficulty |
string | hard, very_hard, or legacy_unrated |
domain |
string | object domain (e.g. road_vehicle, aerial_robotics, astronomy_instrument) |
challenge_tags |
list[string] | geometric challenges the case was authored to contain |
One split: test (36 rows). There is deliberately no train split — this is an evaluation benchmark.
Usage
from datasets import load_dataset
ds = load_dataset("LoYoT/mechbench-36", split="test")
case = ds[0]
case["image"] # PIL image, 1254x1254
case["condition_prompt"] # text conditioning
Typical protocol: condition a method on image (image-to-3D) or on condition_prompt (text-to-3D), and evaluate the produced mesh against the reference view. The spec field states which claims the single reference view can and cannot support, so judges do not grade occluded or prompt-only detail.
Provenance and license
The reference images are AI-generated product renders authored for this benchmark (generation prompts included in reference_generation_prompt); the prompts and specs were written by the authors. No scanned, photographed, or third-party assets are included. Released under CC-BY-4.0.
Citation
@article{procedura2026,
title = {Procedura: A Procedural Modeling Agent for 3D Shape-as-Code Generation},
author = {Lin, Youtian and others},
journal = {arXiv preprint arXiv:2608.26238},
year = {2026}
}