3D Surface Reconstruction from Low-Quality Point Clouds Based on Weak Prior Knowledge

：辛士庆（山东大学）
：2022-11-17 10:30
：腾讯会议ID：581-4735-3770（无密码）

报告人：辛士庆（山东大学）

时  间：11月17日上午10:30-12:00

地  点：腾讯会议ID：581-4735-3770（无密码）

内容摘要:

3D reconstruction is the core technology of Metaverse content construction and the key link of reverse engineering. The report explores how to reconstruct high-fidelity 3D shapes from low-quality point clouds. The core idea is to solve the contradiction between "low data quality" and "high target requirements" based on weak prior knowledge. By absorbing manifold priors in explicit reconstruction, the ambiguity problems caused by thin plates and thin tubes can be resolved; by absorbing the priors with obvious feature lines of CAD models, CAD models with regular structure and clear feature lines can be reconstructed; by absorbing MLP The global prior of the network basis function, self-supervised learning is performed, and the geometric model with high fidelity is restored when the point cloud is sparse, noisy, and severely missing. The report will also summarize the crux of the problems faced by 3D reconstruction and look forward to future development trends.

个人简介：

辛士庆目前是山东大学计算机学院交叉研究中心副教授、博士生导师。研究方向是计算图形学、计算机辅助设计、数字几何处理，尤其是基于多模态三维数据的统一表达、快速计算、鲁棒处理、智能制造。已经培养毕业的博士5名，硕士21名。目前正在培养的博士生7名，硕士生15名，本科科研助手6名。获得2021年度的吴文俊人工智能科学技术奖（2等）以及2022年度山东大学泰山学堂“卓越教师”称号，并被爱思唯尔出版集团评为优秀审稿人（2017）。主持国家自然科学基金3项，发表论文70余篇，其中CCF推荐A类文章27篇(含ACM TOG 11篇，IEEE TVCG 11篇)，1项美国发明专利，5项中国发明发利，获得2015年度的ACM SPM最佳论文奖和2017年度ACM SPM最佳论文奖（第一名）。

 

联系人：曹娟