False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy
This addresses the challenge of limited ground-truth data and false positives in 3D object detection, offering an incremental improvement over existing augmentation methods.
The study tackled the problem of false positives in 3D object detection by developing a false-positive sampling augmentation technique, which reduced false positives and improved detection performance, achieving significant gains over baseline models on KITTI and Waymo Open datasets.
Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this study aims to overcome the limitations of ground-truth sampling and improve the performance of 3D object detection models by developing a new augmentation technique called false-positive sampling. False-positive sampling involves retraining the model using point clouds that are identified as false positives in the model's predictions. We propose an algorithm that utilizes both ground-truth and false-positive sampling and an algorithm for building the false-positive sample database. Additionally, we analyze the principles behind the performance enhancement due to false-positive sampling. Our experiments demonstrate that models utilizing false-positive sampling show a reduction in false positives and exhibit improved object detection performance. On the KITTI and Waymo Open datasets, models with false-positive sampling surpass the baseline models by a large margin.