64 citations · 90 across the 6 of their papers we have counts for
9 papers
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
Van Nguyen Nguyen, Yinlin Hu, Yang Xiao +2
We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these obj…
Robust Differentiable SVD
Wei Wang, Zheng Dang, Yinlin Hu +2
Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether u…
Wide-Depth-Range 6D Object Pose Estimation in Space
Yinlin Hu, Sebastien Speierer, Wenzel Jakob +2
6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric sc…
Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations
Zhigang Li, Yinlin Hu, Mathieu Salzmann +1
While much progress has been made in 6-DoF object pose estimation from a single RGB image, the current leading approaches heavily rely on real-annotation data. As such, they remain…
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square pro…
Single-Stage 6D Object Pose Estimation
Yinlin Hu, Pascal Fua, Wei Wang +1
Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a R…