89 citations · 120 across the 7 of their papers we have counts for
10 papers
Flexible Isosurface Extraction for Gradient-Based Mesh Optimization
Tianchang Shen, Jacob Munkberg, Jon Hasselgren +7
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly co…
ATT3D: Amortized Text-to-3D Object Synthesis
Jonathan Lorraine, Kevin Xie, Xiaohui Zeng +7
Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved hig…
Data-Free Learning of Reduced-Order Kinematics
Nicholas Sharp, Cristian Romero, Alec Jacobson +4
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrate…
Surface Simplification using Intrinsic Error Metrics
Hsueh-Ti Derek Liu, Mark Gillespie, Benjamin Chislett +3
This paper describes a method for fast simplification of surface meshes. Whereas past methods focus on visual appearance, our goal is to solve equations on the surface. Hence, rath…
VectorAdam for Rotation Equivariant Geometry Optimization
Selena Ling, Nicholas Sharp, Alec Jacobson
The Adam optimization algorithm has proven remarkably effective for optimization problems across machine learning and even traditional tasks in geometry processing. At the same tim…
Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range Analysis
Nicholas Sharp, Alec Jacobson
Neural implicit representations, which encode a surface as the level set of a neural network applied to spatial coordinates, have proven to be remarkably effective for optimizing,…
Integer Coordinates for Intrinsic Geometry Processing
Mark Gillespie, Nicholas Sharp, Keenan Crane
In this work, we present a general, efficient, and provably robust representation for intrinsic triangulations. These triangulations have emerged as a powerful tool for robust geom…
DiffusionNet: Discretization Agnostic Learning on Surfaces
Nicholas Sharp, Souhaib Attaiki, Keenan Crane +1
We introduce a new general-purpose approach to deep learning on 3D surfaces, based on the insight that a simple diffusion layer is highly effective for spatial communication. The r…
PointTriNet: Learned Triangulation of 3D Point Sets
Nicholas Sharp, Maks Ovsjanikov
This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space. We present PointTriNet, a differentiable and scalable appro…
The Vector Heat Method
Nicholas Sharp, Yousuf Soliman, Keenan Crane
This paper describes a method for efficiently computing parallel transport of tangent vectors on curved surfaces, or more generally, any vector-valued data on a curved manifold. Mo…