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Found 7 papers, 5 papers with code
Artist-Friendly Relightable and Animatable Neural Heads
An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields.
Fast Neural Representations for Direct Volume Rendering
Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications.
Analytic Ray Splitting for Controlled Precision DVR
For direct volume rendering of post-classified data, we propose an algorithm that analytically splits a ray through a cubical cell at the control points of a piecewise-polynomial transfer function.
Learning Adaptive Sampling and Reconstruction for Volume Visualization
A central challenge in data visualization is to understand which data samples are required to generate an image of a data set in which the relevant information is encoded.
Correspondence-Free Material Reconstruction using Sparse Surface Constraints
We present a method to infer physical material parameters, and even external boundaries, from the scanned motion of a homogeneous deformable object via the solution of an inverse problem.
Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction
In a number of tests using synthetic datasets and real-world measurements, we analyse the robustness of our approach and the convergence behavior of the numerical optimization scheme.
Graphics I.6
Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion.
