|Publication||Coupled Iterative Refinement for 6D Multi-Object Pose Estimation, CVPR 2022|
|Training image modalities||RGB-D|
|Test image modalities||RGB-D|
We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach it- eratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective- N-Point (BD-PnP).
|Computer specifications||2 RTX-3090s|