Search Results for author:
Found 9 papers, 7 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.
Compact 3D Gaussian Representation for Radiance Field
On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality.
Understanding Contrastive Learning Through the Lens of Margins
In this work, we propose a new perspective to understand the role of margins based on gradient analysis.
FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals.
Ranked #2 on
Video Reconstruction
on UVG
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.
Streamable Neural Fields
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations.
NAS-VAD: Neural Architecture Search for Voice Activity Detection
Various neural network-based approaches have been proposed for more robust and accurate voice activity detection (VAD).
Neural Residual Flow Fields for Efficient Video Representations
Inspired by standard video compression algorithms, we propose a neural field architecture for representing and compressing videos that deliberately removes data redundancy through the use of motion information across video frames.
