huangjh-pub/neural-galerkin
[SIGGRAPH Asia 2022] A Neural Galerkin Solver for Accurate Surface Reconstruction
This tool helps 3D graphics professionals, CAD designers, or researchers reconstruct detailed 3D surface models from raw point cloud data. You input scattered 3D points, potentially noisy, and it outputs a clean, accurate triangular mesh representing the underlying surface. It's designed for anyone working with 3D scanning data who needs to convert raw scans into usable mesh models.
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Use this if you need to accurately convert raw point cloud scans, even those with noise or sparse data, into high-quality 3D triangular mesh models for visualization, analysis, or further processing.
Not ideal if you primarily work with existing mesh models and don't need to generate them from point clouds, or if your computational environment doesn't support CUDA-enabled devices.
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Language
Python
License
MIT
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Last pushed
Dec 21, 2022
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