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meshybench

Benchmarks for image-to-3D generation: geometry alignment, texture alignment, mesh details

Details

External ID
1378502223
Source
GITHUB
Company
—
Product
meshybench
Website domain
github.com
Launched
Sept. 20, 2026
Cohort
—
Upvotes
11
Upvotes percentile
0.33858570330514987
Tags
—
Fetched at
Sept. 24, 2026, 5:02 p.m.
Updated at
Sept. 24, 2026, 5:02 p.m.

Enrichment

Theme
creative coding and visual experiments
Vertical
Media & entertainment
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
evaluation benchmark for image-to-3d generation models
Manually corrected
False

Could you build this?

Partial The evaluation pipeline and runner can be vibe-coded, but setting up standardized geometric and texture 3D alignment metrics (Chamfer distance, CLIP-mesh, normal consistency) requires specific 3D computer vision domain knowledge and datasets.

What it would actually take: The benchmark suite requires a 3D processing pipeline (using PyTorch3D, Trimesh, Open3D) that ingests generated meshes and ground truth reference models. It computes quantitative metrics: Chamfer Distance, Earth Mover's Distance, F-Score, Multi-view CLIP/DINO perceptual scores for texture consistency, and mesh topology checks (non-manifold edges, self-intersections). Building this properly requires 3D vision research expertise, automated rendering pipelines (via Blender or headless rasterizers), and a curated evaluation dataset of 3D assets.

Competitors

Other products that read as similar to this one — 1082 launches clear the similarity bar, closest 8 shown.

Attention rank: #663 of 1083 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 326 days after the earliest competitor.

Other launches for this product