7 citations · 8 across the 3 of their papers we have counts for
8 papers
Learning Smooth Neural Functions via Lipschitz Regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson +2
Neural implicit fields have recently emerged as a useful representation for 3D shapes. These fields are commonly represented as neural networks which map latent descriptors and 3D…
Surface Multigrid via Intrinsic Prolongation
Hsueh-Ti Derek Liu, Jiayi Eris Zhang, Mirela Ben-Chen +1
This paper introduces a novel geometric multigrid solver for unstructured curved surfaces. Multigrid methods are highly efficient iterative methods for solving systems of linear eq…
Normal-Driven Spherical Shape Analogies
Hsueh-Ti Derek Liu, Alec Jacobson
This paper introduces a new method to stylize 3D geometry. The key observation is that the surface normal is an effective instrument to capture different geometric styles. Centered…
Chordal Decomposition for Spectral Coarsening
Honglin Chen, Hsueh-Ti Derek Liu, Alec Jacobson +1
We introduce a novel solver to significantly reduce the size of a geometric operator while preserving its spectral properties at the lowest frequencies. We use chordal decompositio…
Neural Subdivision
Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri +2
This paper introduces Neural Subdivision, a novel framework for data-driven coarse-to-fine geometry modeling. During inference, our method takes a coarse triangle mesh as input and…
Cubic Stylization
Hsueh-Ti Derek Liu, Alec Jacobson
We present a 3D stylization algorithm that can turn an input shape into the style of a cube while maintaining the content of the original shape. The key insight is that cubic style…