no code implementations • 21 Sep 2022 • Yilin Liu, Liqiang Lin, Yue Hu, Ke Xie, Chi-Wing Fu, Hao Zhang, Hui Huang
To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning.
1 code implementation • CVPR 2022 • Xingguang Yan, Liqiang Lin, Niloy J. Mitra, Dani Lischinski, Daniel Cohen-Or, Hui Huang
We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds.
2 code implementations • 9 Jul 2021 • Liqiang Lin, Yilin Liu, Yue Hu, Xingguang Yan, Ke Xie, Hui Huang
We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction.
no code implementations • 24 Dec 2020 • Pengdi Huang, Liqiang Lin, Fuyou Xue, Kai Xu, Danny Cohen-Or, Hui Huang
We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels.
no code implementations • 11 Dec 2020 • Liqiang Lin, Pengdi Huang, Chi-Wing Fu, Kai Xu, Hao Zhang, Hui Huang
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e. g., classification and segmentation.
no code implementations • 30 Jun 2020 • Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science.
Ranked #7 on Formation Energy on QM9