Domain Adaptation on Point Clouds for 6D Pose Estimation in Bin-picking Scenarios

Training with simulated data is a common approach in pose estimation research. However, a sim-to-real gap between clean simulated data and noisy real data will seriously weaken the generalization ability of the algorithm, especially for point clouds. To address this problem, this paper proposes a domain adaptive pose estimation network (DAPE-Net). For the feature extracted from the backbone, the network will conduct the real and simulation discrimination based on a feature discriminator, and complete the pose estimation by adversarial training. This makes the network pay more attention to the domain invariant features of simulation and real point clouds to complete domain adaptation. In our experiment, DAPE-Net improved the performance of pose estimation by 10%. To solve the problem that domain adaptation requires a small amount of real data, we propose a scheme that can semiautomatically collect real data in bin-picking scenarios for 6D pose estimation.