5 papers
PINNsur: Physics-Informed Neural Networks for PDEs on Curved Surfaces
Pranav Jain, Navami Kairanda, Peter Yichen Chen +1
Partial differential equations (PDEs) on surfaces are fundamental to scientific computing and geometry processing. A popular approach to solving PDEs on surfaces is the finite elem…
DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids
Navami Kairanda, Shanthika Naik, Marc Habermann +3
We present a novel differentiable grid-based representation for efficiently solving differential equations (DEs). Widely used architectures for neural solvers, such as sinusoidal n…
Ev4DGS: Novel-view Rendering of Non-Rigid Objects from Monocular Event Streams
Takuya Nakabayashi, Navami Kairanda, Hideo Saito +1
Event cameras offer various advantages for novel view rendering compared to synchronously operating RGB cameras, and efficient event-based techniques supporting rigid scenes have b…
Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields
Navami Kairanda, Marc Habermann, Shanthika Naik +2
3D reconstruction of highly deformable surfaces (e.g. cloths) from monocular RGB videos is a challenging problem, and no solution provides a consistent and accurate recovery of fin…
NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory
Navami Kairanda, Marc Habermann, Christian Theobalt +1
Despite existing 3D cloth simulators producing realistic results, they predominantly operate on discrete surface representations (e.g. points and meshes) with a fixed spatial resol…