.png){kind=icon}
.png){kind=icon}
Return
Implicit Surface Representation Using Epanechnikov Mixture Regression
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2024.3421350.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a regression-based implicit surface representation using mixture-of-experts based on the Epanechnikov kernel (EK), a mathematical framework that does not depend on neural networks. The modeling method is implemented using signed distance fields (SDF), modeled using the expectation-maximization algorithm to iterate an optimal set of parameters of Epanechnikov mixture regression. The proposed pipeline achieves better reconstruction than the SDF itself and can be upsampled through mixture-of-experts-based interpolation without extra parameters and processing. Furthermore, the proposed method can efficiently realize data compression compared to meshes and SDF. As for the kernel theory, EK demonstrates a more accurate surface recovery than the Gaussian ones, which expands the applications for Epanechnikov-related theories and also shows potential for theoretical substitution for Gaussian-based modeling and representation.
Keywords:
Three-dimensional displays
Shape
Solid modeling
Image reconstruction
Kernel
Optimization
Surface reconstruction
3D compression
3D reconstruction
3D shape representation
epanechnikov mixture regression
implicit surface representation
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W