Search Results for author:
Found 8 papers, 1 papers with code
Semantic Human Mesh Reconstruction with Textures
SHERT applies semantic- and normal-based sampling between the detailed surface (e. g. mesh and SDF) and the corresponding SMPL-X model to obtain a partially sampled semantic mesh and then generates the complete semantic mesh by our specifically designed self-supervised completion and refinement networks.
360-GS: Layout-guided Panoramic Gaussian Splatting For Indoor Roaming
This technique typically takes perspective images as input and optimizes a set of 3D elliptical Gaussians by splatting them onto the image planes, resulting in 2D Gaussians.
On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy
This paper addresses the projection error function of 3D Gaussian Splatting, commencing with the residual error from the first-order Taylor expansion of the projection function.
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
Rendering Discrete Participating Media with Geometrical Optics Approximation
We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles.
Graphics Optics
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns.
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment
The multi-factor model is a widely used model in quantitative investment.
Policy Search by Target Distribution Learning for Continuous Control
We observe that several existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may suffer from overly large gradients when the current policy is close to deterministic (even in some very simple environments), leading to an unstable training process.
