Search Results for author:
Found 7 papers, 6 papers with code
Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
With model compression techniques such as quantization and entropy coding, we consistently show over 25x reduced storage and enhanced rendering speed compared to 3DGS for static scenes, while maintaining the quality of the scene representation.
Continuous Memory Representation for Anomaly Detection
To tackle all of the above challenges, we propose CRAD, a novel anomaly detection method for representing normal features within a "continuous" memory, enabled by transforming spatial features into coordinates and mapping them to continuous grids.
Ranked #33 on
Anomaly Detection
on MVTec AD
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.
Ranked #9 on
Novel View Synthesis
on Tanks and Temples
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 #3 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.
