UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D Images in Unsupervised Reconstruction

no code implementations • 10 Dec 2023 • Hongbin Lin, Juangui Xu, Qingfeng Xu, Zhengyu Hu, Handing Xu, Yunzhi Chen, Yongjun Hu, Zhenguo Nie

Our model significantly cuts down the training costs tied to supervised approaches and introduces RGB coloration to 3D point clouds, enriching the visual experience.

3D Reconstruction Point Cloud Generation

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field with Attention Mechanism and Perceptual Features

1 code implementation • 12 Mar 2021 • Qingfeng Xu, Zhenguo Nie, Handing Xu, Haosu Zhou, Xinjun Liu

In stress field analysis, the finite element analysis is a crucial approach, in which the mesh-density has a significant impact on the results.

A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations

no code implementations • 30 Sep 2020 • Haosu Zhou, Qingfeng Xu, Nan Li

It is demonstrated that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness.

BIG-bench Machine Learning Informativeness