13 citations · 16 across the 4 of their papers we have counts for
4 papers
PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching
Pedro Castro, Tae-Kyun Kim
Estimating the pose of an unseen object is the goal of the challenging one-shot pose estimation task. Previous methods have heavily relied on feature matching with great success. H…
CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement Transformers
Pedro Castro, Tae-Kyun Kim
Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-comp…
Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images
Juil Sock, Guillermo Garcia-Hernando, Anil Armagan +1
Most successful approaches to estimate the 6D pose of an object typically train a neural network by supervising the learning with annotated poses in real world images. These annota…
Accurate 6D Object Pose Estimation by Pose Conditioned Mesh Reconstruction
Pedro Castro, Anil Armagan, Tae-Kyun Kim
Current 6D object pose methods consist of deep CNN models fully optimized for a single object but with its architecture standardized among objects with different shapes. In contras…