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The document discusses the introduction of IoU loss for 2D and 3D object detection to bridge the gap between training and testing performance metrics. It highlights the limitations of traditional L1 and L2 loss functions for bounding box regression, particularly for rotated bounding boxes, and presents a unified IoU loss framework that improves detection accuracy. The proposed IoU loss has been validated through integration with state-of-the-art 3D object detection frameworks on the KITTI benchmark, demonst

Author

li1131637780

Language

EN