no code implementations • 24 Jun 2024 • YiFei Gao, Jie Ou, Lei Wang, Yuting Xiao, Zhiyuan Xiang, Ruiting Dai, Jun Cheng
LSI uses Singular Value Decomposition to extract singular values of the weights and make them learnable to help weights compensate each other conditioned on activation.
no code implementations • 18 Mar 2024 • Yuting Xiao, Xuan Wang, Jiafei Li, Hongrui Cai, Yanbo Fan, Nan Xue, Minghui Yang, Yujun Shen, Shenghua Gao
To this end, we propose a novel approach, GauMesh, to bridge the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes.
no code implementations • 29 Aug 2023 • Yuting Xiao, Jingwei Xu, Zehao Yu, Shenghua Gao
This paper presents \textbf{DebSDF} to address these challenges, focusing on the utilization of uncertainty in monocular priors and the bias in SDF-based volume rendering.
no code implementations • 26 Nov 2022 • Yuting Xiao, Yiqun Zhao, Yanyu Xu, Shenghua Gao
In the first stage, we focus on geometry reconstruction based on SDF representation, which would lead to a good geometry surface of the scene and also a sharp density.
no code implementations • 18 Jan 2022 • Yuting Xiao, Jiale Xu, Shenghua Gao
Taylor3DNet exploits a set of discrete landmark points and their corresponding Taylor series coefficients to represent the implicit field of a 3D shape, and the number of landmark points is independent of the resolution of the iso-surface extraction.
1 code implementation • 10 Dec 2020 • Yuting Xiao, Yanyu Xu, Ziming Zhong, Weixin Luo, Jiawei Li, Shenghua Gao
In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation.
1 code implementation • ECCV 2020 • Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao
In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.
no code implementations • 6 Aug 2019 • Tianyang Zhang, Huazhu Fu, Yitian Zhao, Jun Cheng, Mengjie Guo, Zaiwang Gu, Bing Yang, Yuting Xiao, Shenghua Gao, Jiang Liu
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.