64 citations · 113 across the 22 of their papers we have counts for
6 papers · 1 filter
Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation
Yang Hai, Rui Song, Jiaojiao Li +2
Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their a…
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation
Yang Hai, Rui Song, Jiaojiao Li +1
Most recent 6D object pose methods use 2D optical flow to refine their results. However, the general optical flow methods typically do not consider the target's 3D shape informatio…
Rigidity-Aware Detection for 6D Object Pose Estimation
Yang Hai, Rui Song, Jiaojiao Li +2
Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection meth…
NOPE: Novel Object Pose Estimation from a Single Image
Van Nguyen Nguyen, Thibault Groueix, Yinlin Hu +2
The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects. To addr…
Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation
Fulin Liu, Yinlin Hu, Mathieu Salzmann
Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differe…
Modular Quantization-Aware Training for 6D Object Pose Estimation
Saqib Javed, Chengkun Li, Andrew Price +2
Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D pose…