CVL-UESTC/FR-INR
CVPR 2024-Improved Implicit Neural Representation with Fourier Reparameterized Training
This project helps computer vision researchers and practitioners who use Implicit Neural Representations (INRs) for tasks like image or 3D shape reconstruction. It provides a new training method, Fourier Reparameterization, to improve how INRs learn details, resulting in outputs with richer textures and fewer visual flaws. You input training data (e.g., images, 3D occupancy grids) and get a more accurate and detailed INR model.
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Use this if you are working with Implicit Neural Representations and need to overcome issues like blurry outputs or poor detail reproduction, specifically addressing the 'spectral bias' problem.
Not ideal if you are looking for a completely new INR architecture rather than an improved training technique for existing ones.
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Python
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May 23, 2025
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