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Found 9 papers, 6 papers with code
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets.
SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting
Our model addresses these challenges by effectively integrating explicit 3D representations with self-supervised depth and pose estimation techniques, resulting in reciprocal improvements in both pose accuracy and 3D reconstruction quality.
Freq-Mip-AA : Frequency Mip Representation for Anti-Aliasing Neural Radiance Fields
The proposed method, FreqMipAA, utilizes scale-specific low-pass filtering (LPF) and learnable frequency masks.
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.
Separable Physics-Informed Neural Networks
Furthermore, we present that SPINN can solve a chaotic (2+1)-d Navier-Stokes equation significantly faster than the best-performing prior method (9 minutes vs 10 hours in a single GPU), maintaining accuracy.
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.
