1 code implementation • 28 Feb 2024 • Joo Chan Lee, Taejune Kim, Eunbyung Park, Simon S. Woo, Jong Hwan Ko
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.
1 code implementation • 25 Nov 2023 • Joo Chan Lee, Daniel Rho, Seungtae Nam, Jong Hwan Ko, Eunbyung Park
Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals.
1 code implementation • 22 Nov 2023 • Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, Eunbyung Park
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.
1 code implementation • 23 Dec 2022 • Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Eunbyung Park
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
1 code implementation • CVPR 2023 • Daniel Rho, Byeonghyeon Lee, Seungtae Nam, Joo Chan Lee, Jong Hwan Ko, Eunbyung Park
There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees.