6 papers
DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent
Jia-Wei Liao, Li-Xuan Peng, Mei-Heng Yueh +3
Recently, diffusion models have been widely adopted in generative modeling and have served as foundational models for many image generation tasks. To control the generation without…
An Improved Variational Method for Image Denoising
Jing-En Huang, Jia-Wei Liao, Ku-Te Lin +2
The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel inte…
Square-Domain Area-Preserving Parameterization for Genus-Zero and Genus-One Closed Surfaces
Shu-Yung Liu, Mei-Heng Yueh
The parameterization of closed surfaces typically requires either multiple charts or a non-planar domain to achieve a seamless global mapping. In this paper, we propose a numerical…
Toroidal area-preserving parameterizations of genus-one closed surfaces
Marco Sutti, Mei-Heng Yueh
We consider the problem of computing toroidal area-preserving parameterizations of genus-one closed surfaces. We propose four algorithms based on Riemannian geometry: the projected…
Spherical Area-Preserving Parameterization via Energy Minimization
Shu-Yung Liu, Mei-Heng Yueh
We propose a novel method, called spherical authalic energy minimization (SAEM), for computing spherical area-preserving parameterizations of genus-zero closed surfaces, with stron…
Energy-Based Distortion-Balancing Parameterization for Open Surfaces
Shu-Yung Liu, Mei-Heng Yueh
Surface parameterization is a fundamental concept in fields such as differential geometry and computer graphics. It involves mapping a surface in three-dimensional space onto a two…