no code implementations • 5 Mar 2024 • Xiaoyu Zhan, Jianxin Yang, Yuanqi Li, Jie Guo, Yanwen Guo, Wenping Wang
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.
no code implementations • 1 Feb 2024 • Letian Huang, Jiayang Bai, Jie Guo, Yuanqi Li, Yanwen Guo
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.
no code implementations • 1 Feb 2024 • Jiayang Bai, Letian Huang, Jie Guo, Wen Gong, Yuanqi Li, Yanwen Guo
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.
no code implementations • 17 Mar 2022 • Yuanqi Li, Jianwei Guo, Xinran Yang, Shun Liu, Jie Guo, Xiaopeng Zhang, Yanwen Guo
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
no code implementations • 24 Feb 2021 • Jie Guo, Bingyang Hu, Yanjun Chen, Yuanqi Li, Yanwen Guo, Ling-Qi Yan
We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles.
Graphics Optics
1 code implementation • 3 Oct 2020 • Chuheng Zhang, Yuanqi Li, Xi Chen, Yifei Jin, Pingzhong Tang, Jian Li
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.
no code implementations • 9 Feb 2020 • Tianping Zhang, Yuanqi Li, Yifei Jin, Jian Li
The multi-factor model is a widely used model in quantitative investment.
no code implementations • 27 May 2019 • Chuheng Zhang, Yuanqi Li, Jian Li
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.