CycleBEV: Regularizing View Transformation Networks via View Cycle Consistency for Bird's-Eye-View Semantic Segmentation

Researchers propose CycleBEV, a new regularization framework that improves bird's-eye-view semantic segmentation for autonomous driving by using cycle consistency to enhance view transformation networks. The method shows significant improvements up to 4.86 mIoU without increasing inference complexity.