4 papers · 1 filter
BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation
Van Nguyen Nguyen, Stephen Tyree, Andrew Guo +16
We present the evaluation methodology, datasets and results of the BOP Challenge 2024, the 6th in a series of public competitions organized to capture the state of the art in 6D ob…
Co-op: Correspondence-based Novel Object Pose Estimation
Sungphill Moon, Hyeontae Son, Dongcheol Hur +1
We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD mo…
GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects
Sungphill Moon, Hyeontae Son, Dongcheol Hur +1
Despite the progress of learning-based methods for 6D object pose estimation, the trade-off between accuracy and scalability for novel objects still exists. Specifically, previous…
Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks
Youngbin Park, Sungphill Moon, Il Hong Suh
Kinect skeleton tracker is able to achieve considerable human body tracking performance in convenient and a low-cost manner. However, The tracker often captures unnatural human pos…