When Regression Meets Manifold Learning for Object Recognition and Pose
Estimation

When Regression Meets Manifold Learning for Object Recognition and Pose Estimation

16 May 2018

Sergey Zakharov

Shadi Albarqouni

Nassir Navab

Papers citing "When Regression Meets Manifold Learning for Object Recognition and Pose Estimation"

7 / 7 papers shown

Title
PViT-6D: Overclocking Vision Transformers for 6D Pose Estimation with Confidence-Level Prediction and Pose Tokens Sebastian Stapf Tobias Bauernfeind Marco Riboldi ViT 25 1 0 29 Nov 2023
A Probabilistic Rotation Representation for Symmetric Shapes With an Efficiently Computable Bingham Loss Function Hiroya Sato Takuya Ikeda Koichi Nishiwaki 29 2 0 30 May 2023
6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization Jun Yang Wenjie Xue Sahar Ghavidel Steven L. Waslander 42 11 0 20 Oct 2022
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation I. Shugurov Sergey Zakharov Slobodan Ilic 20 56 0 06 Jul 2022
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework I. Shugurov Fu Li Benjamin Busam Slobodan Ilic 40 85 0 29 Mar 2022
Probabilistic Rotation Representation With an Efficiently Computable Bingham Loss Function and Its Application to Pose Estimation Hiroya Sato Takuya Ikeda Koichi Nishiwaki 41 1 0 09 Mar 2022
6D Object Pose Regression via Supervised Learning on Point Clouds Ge Gao M. Lauri Yulong Wang Xiaolin Hu Jianwei Zhang Simone Frintrop 3DPC 30 81 0 24 Jan 2020