5 citations · 13 across the 6 of their papers we have counts for
6 papers
CageNet: A Meta-Framework for Learning on Wild Meshes
Michal Edelstein, Hsueh-Ti Derek Liu, Mirela Ben-Chen
Learning on triangle meshes has recently proven to be instrumental to a myriad of tasks, from shape classification, to segmentation, to deformation and animation, to mention just a…
Designing 3D Anisotropic Frame Fields with Odeco Tensors
Haikuan Zhu, Hongbo Li, Hsueh-Ti Derek Liu +3
This paper introduces a method to synthesize a 3D tensor field within a constrained geometric domain represented as a tetrahedral mesh. Whereas previous techniques optimize for iso…
Simplifying Textured Triangle Meshes in the Wild
Hsueh-Ti Derek Liu, Xiaoting Zhang, Cem Yuksel
This paper introduces a method for simplifying textured surface triangle meshes in the wild while maintaining high visual quality. While previous methods achieve excellent results…
An Intrinsic Vector Heat Network
Alexander Gao, Maurice Chu, Mubbasir Kapadia +2
Vector fields are widely used to represent and model flows for many science and engineering applications. This paper introduces a novel neural network architecture for learning tan…
A Unified Differentiable Boolean Operator with Fuzzy Logic
Hsueh-Ti Derek Liu, Maneesh Agrawala, Cem Yuksel +5
This paper presents a unified differentiable boolean operator for implicit solid shape modeling using Constructive Solid Geometry (CSG). Traditional CSG relies on min, max operator…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…