False Positive Sampling-Based Data Augmentation for Enhanced 3D Object Detection Accuracy

3D object detection plays a pivotal role in autonomous driving, and its accuracy has improved substantially in recent years. Among existing augmentation methods for training 3D object detection model, ground truth sampling significantly improves model performance by increasing the number of positive samples in a scene and alleviating class imbalance. However, our experiments reveal that ground truth sampling can excessively expand the model decision boundary, leading to a notable increase in false positives. To address this issue, we propose a novel data augmentation technique called false positive sampling, which retrains the model using point clouds misclassified as positive during inference. This approach effectively reduces false positives without sacrificing the number of true positives, resulting in considerable performance gains. Furthermore, false-positive sampling maximizes the class imbalance mitigation effect of ground-truth sampling by leveraging these challenging examples. Our method also improves the model's ability to semantically understand difficult samples that typically cause confusion. Experimental results on standard 3D object detection benchmarks demonstrate the effectiveness of the proposed algorithm in achieving robust and accurate detection.