astaka-pe/Dual-DMP
Learning Self-prior for Mesh Denoising using Dual Graph Convolutional Networks [ECCV 2022]
This tool helps 3D designers and engineers clean up noisy 3D mesh models, which often result from 3D scanning or CAD modeling errors. You provide a noisy 3D mesh file (e.g., an OBJ file) as input, and it outputs a much smoother, denoised version of the same 3D model. This is ideal for anyone working with digital twins, 3D printing, or virtual reality who needs high-quality, artifact-free 3D geometries.
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Use this if you need to automatically remove imperfections and noise from a single 3D mesh model to improve its visual quality or prepare it for manufacturing.
Not ideal if you need to reconstruct a 3D model from point clouds or perform complex mesh repair beyond simple denoising.
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44
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4
Language
Python
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Last pushed
Apr 13, 2025
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