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Found 20 papers, 10 papers with code
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
As such, we propose the direct perturbation of physical parameters that underly image formation: lighting and geometry.
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.
Ranked #4 on
Single-View 3D Reconstruction
on ShapeNet
NiLBS: Neural Inverse Linear Blend Skinning
In this technical report, we investigate efficient representations of articulated objects (e. g. human bodies), which is an important problem in computer vision and graphics.
Neural Subdivision
During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch.
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input.
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
We introduce Deformable Tetrahedral Meshes (DefTet) as a particular parameterization that utilizes volumetric tetrahedral meshes for the reconstruction problem.
Complementary Dynamics
Our method does not require a particular type of rig and adds secondary effects to skeletal animations, cage-based deformations, wire deformers, motion capture data, and rigid-body simulations.
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes
We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality.
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.
Learning Smooth Neural Functions via Lipschitz Regularization
The latent descriptor of a neural field acts as a deformation handle for the 3D shape it represents.
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.
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit.
Variable Bitrate Neural Fields
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations.
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
We introduce Breaking Bad, a large-scale dataset of fractured objects.
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.
Neural Progressive Meshes
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e. g., 3D meshes, over the Internet.
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties.
Neural Stochastic Screened Poisson Reconstruction
Reconstructing a surface from a point cloud is an underdetermined problem.
VecFusion: Vector Font Generation with Diffusion
We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.
Compact Neural Graphics Primitives with Learned Hash Probing
Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid.
