1 code implementation • 14 Dec 2023 • Ting Pan, Lulu Tang, Xinlong Wang, Shiguang Shan
The semantic token is responsible for learning the semantic priors in a predefined concept space.
no code implementations • 22 Apr 2022 • Luqing Luo, Lulu Tang, Wanyi Zhou, Shizheng Wang, Zhi-Xin Yang
In this work, the flexible upsampling rates are achieved via edge vector based affine combinations, and a novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.
2 code implementations • CVPR 2022 • Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie zhou, Jiwen Lu
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Ranked #13 on Few-Shot 3D Point Cloud Classification on ModelNet40 5-way (10-shot) (using extra training data)
3D Point Cloud Linear Classification Few-Shot 3D Point Cloud Classification +2
1 code implementation • ICCV 2021 • Luqing Luo, Lulu Tang, Wanyi Zhou, Shizheng Wang, Zhi-Xin Yang
In this work, the arbitrary point clouds upsampling rates are achieved via edge-vector based affine combinations, and a novel design of Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.
no code implementations • 14 Jan 2020 • Lulu Tang, Ke Chen, Chaozheng Wu, Yu Hong, Kui Jia, Zhi-Xin Yang
Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner.