Reconstructing a surface from a point cloud is an underdetermined proble...
We conduct a controlled crowd-sourced experiment of COVID-19 case data
v...
Neural Radiance Fields (NeRFs) have shown promise in applications like v...
The recent proliferation of 3D content that can be consumed on hand-held...
This paper describes a method for fast simplification of surface meshes....
Physical systems ranging from elastic bodies to kinematic linkages are
d...
We introduce Breaking Bad, a large-scale dataset of fractured objects. O...
We introduce a statistical extension of the classic Poisson Surface
Reco...
Neural approximations of scalar and vector fields, such as signed distan...
Learning to autonomously assemble shapes is a crucial skill for many rob...
The rise of geometric problems in machine learning has necessitated the
...
Neural implicit fields have recently emerged as a useful representation ...
Neural implicit representations, which encode a surface as the level set...
Drawing a direct analogy with the well-studied vibration or elastic mode...
Communicating linear algebra in written form is challenging: mathematici...
We present a new approach for modelling musculoskeletal anatomy. Unlike
...
This paper introduces a novel geometric multigrid solver for unstructure...
This paper introduces a new method to stylize 3D geometry. The key
obser...
Neural signed distance functions (SDFs) are emerging as an effective
rep...
We present Diffusion Structures, a family of resilient shell structures ...
3D shape representations that accommodate learning-based 3D reconstructi...
A neural implicit outputs a number indicating whether the given query po...
We present a novel approach to enrich arbitrary rig animations with
elas...
We introduce a novel solver to significantly reduce the size of a geomet...
EMU is an efficient and scalable model to simulate bulk musculoskeletal
...
This note summarizes the steps to computing the best-fitting affine
refl...
This paper introduces Neural Subdivision, a novel framework for data-dri...
We propose a novel algorithm to efficiently generate hidden structures t...
In this technical report, we investigate efficient representations of
ar...
The biharmonic equation with Dirichlet and Neumann boundary conditions
d...
We present a 3D stylization algorithm that can turn an input shape into ...
Many machine learning models operate on images, but ignore the fact that...
Computer animation in conjunction with 3D printing has the potential to
...
We present RodSteward, a design-to-assembly system for creating
furnitur...
Current quadratic smoothness energies for curved surfaces either exhibit...
We introduce a novel approach to measure the behavior of a geometric ope...
We present the first algorithm for designing volumetric Michell Trusses....
Many machine learning classifiers are vulnerable to adversarial attacks,...
Many machine learning image classifiers are vulnerable to adversarial
at...
Many tasks in geometry processing are modeled as variational problems so...
In geometry processing, smoothness energies are commonly used to model
s...
Empirically validating new 3D-printing related algorithms and implementa...
The generalized winding number function measures insideness for arbitrar...
Jacobson et al. [JKSH13] hypothesized that the local coherency of the
ge...