LIA-DiTella/DiffUDF
Repo for the CVPR 2024 paper: "DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling"
This tool helps 3D graphics professionals and researchers create highly detailed and smooth 3D object representations from initial mesh models or point clouds. It takes a 3D mesh or a sampled point cloud as input and outputs a neural network that represents the object's shape as a Differentiable Unsigned Distance Field (UDF). Users can then render the object with various lighting and curvature options, or extract dense, oriented point clouds for further processing.
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Use this if you need to accurately reconstruct complex 3D shapes from existing meshes or sparse point clouds and require fine-grained control over rendering and surface properties.
Not ideal if you need a quick, low-detail 3D model for immediate use without deep reconstruction, or if you lack a Linux machine with a CUDA-enabled GPU.
Stars
27
Forks
2
Language
Python
License
MIT
Category
Last pushed
Oct 15, 2024
Commits (30d)
0
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