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Found 13 papers, 5 papers with code
Neurally Integrated Finite Elements for Differentiable Elasticity on Evolving Domains
We present an elastic simulator for domains defined as evolving implicit functions, which is efficient, robust, and differentiable with respect to both shape and material.
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
In particular, our vertex embeddings generate cyclic neighbor relationships in a halfedge mesh representation, which gives a guarantee of edge-manifoldness and the ability to represent general polygonal meshes.
3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes
The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more.
Simplicits: Mesh-Free, Geometry-Agnostic, Elastic Simulation
The proliferation of 3D representations, from explicit meshes to implicit neural fields and more, motivates the need for simulators agnostic to representation.
Adaptive Shells for Efficient Neural Radiance Field Rendering
We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band.
TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models.
Flexible Isosurface Extraction for Gradient-Based Mesh Optimization
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 common paradigm in applications including photogrammetry, generative modeling, and inverse physics.
ATT3D: Amortized Text-to-3D Object Synthesis
Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields.
Data-Free Learning of Reduced-Order Kinematics
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrated on much lower-dimensional subspaces.
VectorAdam for Rotation Equivariant Geometry Optimization
We demonstrate this approach on problems in machine learning and traditional geometric optimization, showing that equivariant VectorAdam resolves the artifacts and biases of traditional Adam when applied to vector-valued data, with equivalent or even improved rates of convergence.
Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range Analysis
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, compressing, and generating 3D geometry.
DiffusionNet: Discretization Agnostic Learning on Surfaces
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.
PointTriNet: Learned Triangulation of 3D Point Sets
This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space.
