Sim2Real Instance-Level Style Transfer for 6D Pose Estimation
This addresses a domain-specific problem for robotics and computer vision by incrementally enhancing synthetic data quality for training.
The paper tackles the domain gap between synthetic and real data in 6D pose estimation by introducing an instance-level style transfer method, resulting in significant improvements in pose estimation performance and image realism.
In recent years, synthetic data has been widely used in the training of 6D pose estimation networks, in part because it automatically provides perfect annotation at low cost. However, there are still non-trivial domain gaps, such as differences in textures/materials, between synthetic and real data. These gaps have a measurable impact on performance. To solve this problem, we introduce a simulation to reality (sim2real) instance-level style transfer for 6D pose estimation network training. Our approach transfers the style of target objects individually, from synthetic to real, without human intervention. This improves the quality of synthetic data for training pose estimation networks. We also propose a complete pipeline from data collection to the training of a pose estimation network and conduct extensive evaluation on a real-world robotic platform. Our evaluation shows significant improvement achieved by our method in both pose estimation performance and the realism of images adapted by the style transfer.