Search Results for author:
Found 5 papers, 3 papers with code
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
In this work, we present mip-Grid, a novel approach that integrates anti-aliasing techniques into grid-based representations for radiance fields, mitigating the aliasing artifacts while enjoying fast training time.
Coordinate-Aware Modulation for Neural Fields
Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals.
Masked Wavelet Representation for Compact Neural Radiance Fields
There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees.
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
SPINN operates on a per-axis basis instead of point-wise processing in conventional PINNs, decreasing the number of network forward passes.
Streamable Neural Fields
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations.
