alekseizhuravlev/denoising-functional-maps
[CVPR'2025] Denoising Functional Maps: Diffusion Models for Shape Correspondence
This project helps researchers and practitioners in computer graphics and 3D vision accurately align different 3D shapes, even when they have noise or variations. It takes two 3D mesh models as input and produces a detailed, point-to-point correspondence showing how one shape maps onto the other. This is ideal for those working with 3D model analysis, animation, or medical imaging.
No commits in the last 6 months.
Use this if you need to find precise correspondences between complex 3D shapes, especially when dealing with noisy or slightly different models.
Not ideal if you only need rough shape alignment or are working with simple 2D images instead of 3D meshes.
Stars
16
Forks
—
Language
Python
License
MIT
Category
Last pushed
Jul 31, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/alekseizhuravlev/denoising-functional-maps"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
quantgirluk/aleatory
📦 Python library for Stochastic Processes Simulation and Visualisation
blei-lab/treeffuser
Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression...
TuftsBCB/RegDiffusion
Diffusion model for gene regulatory network inference.
yuanchenyang/smalldiffusion
Simple and readable code for training and sampling from diffusion models
chairc/Integrated-Design-Diffusion-Model
IDDM (Industrial, landscape, animate, latent diffusion), support LDM, DDPM, DDIM, PLMS, webui...