mabaorui/Noise2NoiseMapping
[ICML'23 Oral] Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
This helps researchers and engineers working with 3D models to improve the quality of 3D reconstructions from imperfect sensor data. It takes in 3D point cloud data that might have errors or noise and produces a cleaner, more accurate signed distance function (SDF) representation. This tool is for anyone who needs to convert raw, noisy 3D scans into smooth, mathematically defined surfaces for tasks like CAD modeling or simulation.
125 stars. No commits in the last 6 months.
Use this if you need to accurately reconstruct smooth 3D surfaces from noisy point cloud scans obtained from sensors like LiDAR or 3D scanners.
Not ideal if your input data is already perfectly clean or if you are working with 2D images instead of 3D point clouds.
Stars
125
Forks
8
Language
Python
License
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Last pushed
Aug 21, 2023
Commits (30d)
0
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