GSNR constructs a null-restricted graph Laplacian and projects onto its smoothest modes to regularize only the null-space part of inverse problem solutions, yielding up to 4.3 dB PSNR gains when plugged into PnP, DIP, and diffusion solvers.
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RT-Splatting adds a disentangled occupancy-opacity factorization and specular-aware gradient gating to 3D Gaussian Splatting, enabling joint high-fidelity reflection and transmission in real-time novel view synthesis.
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GSNR: Graph Smooth Null-Space Representation for Inverse Problems
GSNR constructs a null-restricted graph Laplacian and projects onto its smoothest modes to regularize only the null-space part of inverse problem solutions, yielding up to 4.3 dB PSNR gains when plugged into PnP, DIP, and diffusion solvers.
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RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian Splatting
RT-Splatting adds a disentangled occupancy-opacity factorization and specular-aware gradient gating to 3D Gaussian Splatting, enabling joint high-fidelity reflection and transmission in real-time novel view synthesis.