Computational optimal transport: With applications to data science
Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
[HTML][HTML] Point cloud generation for meshfree methods: An overview
P Suchde, T Jacquemin, O Davydov - Archives of Computational Methods …, 2023 - Springer
Meshfree methods are becoming an increasingly popular alternative to mesh-based
methods of numerical simulation. The biggest stated advantage of meshfree methods is the …
methods of numerical simulation. The biggest stated advantage of meshfree methods is the …
Meshcnn: a network with an edge
Polygonal meshes provide an efficient representation for 3D shapes. They explicitly
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …
Learning representations and generative models for 3d point clouds
P Achlioptas, O Diamanti… - … on machine learning, 2018 - proceedings.mlr.press
Three-dimensional geometric data offer an excellent domain for studying representation
learning and generative modeling. In this paper, we look at geometric data represented as …
learning and generative modeling. In this paper, we look at geometric data represented as …
Tailornet: Predicting clothing in 3d as a function of human pose, shape and garment style
In this paper, we present TailorNet, a neural model which predicts clothing deformation in
3D as a function of three factors: pose, shape and style (garment geometry), while retaining …
3D as a function of three factors: pose, shape and style (garment geometry), while retaining …
Large steps in inverse rendering of geometry
Inverse reconstruction from images is a central problem in many scientific and engineering
disciplines. Recent progress on differentiable rendering has led to methods that can …
disciplines. Recent progress on differentiable rendering has led to methods that can …
Cvxnet: Learnable convex decomposition
Any solid object can be decomposed into a collection of convex polytopes (in short,
convexes). When a small number of convexes are used, such a decomposition can be …
convexes). When a small number of convexes are used, such a decomposition can be …
Cagenerf: Cage-based neural radiance field for generalized 3d deformation and animation
While implicit representations have achieved high-fidelity results in 3D rendering, it remains
challenging to deforming and animating the implicit field. Existing works typically leverage …
challenging to deforming and animating the implicit field. Existing works typically leverage …
A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis
Structural finite-element analysis (FEA) has been widely used to study the biomechanics of
human tissues and organs, as well as tissue–medical device interactions, and treatment …
human tissues and organs, as well as tissue–medical device interactions, and treatment …
Geometry processing with neural fields
Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …
representation. Manipulating meshes, however, requires one to maintain high quality in the …