Geometry-Insensitive RPN Prototypes for Domain Adaptive 3D Object Detection

The region proposal network (RPN) plays a critical role in object detection for a two-stage domain adaptive 3D object detector. However, current methods usually minimize the disparity between source and target domains by reducing the bias in intrinsic geometric information or by undertaking feature alignment according to the geometric disparity but ignore the transferability of RPN-related features and neglect the discriminability between foreground and background, resulting in generating low-quality RPN proposals. Thus, we propose a novel domain adaptation method to distinguish the discriminability between foreground and background. It could implicitly avoid the geometric disparity of objects in feature alignment. Specifically, we first construct learnable and geometry-insensitive foreground RPN prototype and background RPN prototype. Then, we enforce the foreground RPN features and background RPN features to align with the foreground RPN prototype and background RPN prototype, respectively. By this way, the distributional discrepancy is effectively decreased and the adaptability is promoted for existing 3D detectors. We demonstrate that our approach achieves promising results compared with other domain adaptation works on multiple cross-domain detection scenarios.