no code implementations • 5 Oct 2023 • Zhiwen Fan, Panwang Pan, Peihao Wang, Yifan Jiang, Hanwen Jiang, Dejia Xu, Zehao Zhu, Dilin Wang, Zhangyang Wang
To address this challenge, we introduce PF-GRT, a new Pose-Free framework for Generalizable Rendering Transformer, eliminating the need for pre-computed camera poses and instead leveraging feature-matching learned directly from data.
1 code implementation • 13 Jun 2023 • Panwang Pan, Zhiwen Fan, Brandon Y. Feng, Peihao Wang, Chenxin Li, Zhangyang Wang
The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many applications in robotics and augmented reality.
1 code implementation • 25 May 2023 • Zhiwen Fan, Panwang Pan, Peihao Wang, Yifan Jiang, Dejia Xu, Hanwen Jiang, Zhangyang Wang
To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE).
no code implementations • ICCV 2023 • Chenxin Li, Brandon Y. Feng, Zhiwen Fan, Panwang Pan, Zhangyang Wang
Recent advances in neural rendering imply a future of widespread visual data distributions through sharing NeRF model weights.