Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation
This work addresses a key challenge in computer vision for robotics, offering an incremental improvement in pose estimation methods.
The paper tackles the problem of finding effective output parameterizations for single-shot 6D object pose estimation, achieving state-of-the-art performance on two public benchmark datasets and enabling real-world robotic grasping without additional refinement.
Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge. In this work, we propose different novel parameterizations for the output of the neural network for single shot 6D object pose estimation. Our learning-based approach achieves state-of-the-art performance on two public benchmark datasets. Furthermore, we demonstrate that the pose estimates can be used for real-world robotic grasping tasks without additional ICP refinement.