DriveFlow: 用于自动驾驶中鲁棒3D目标检测的校正流适应
DriveFlow: Rectified Flow Adaptation for Robust 3D Object Detection in Autonomous Driving
AI总结：
DriveFlow通过校正流适应方法提升自动驾驶中3D目标检测的鲁棒性，利用预训练文本到图像流模型实现高效的数据增强。
摘要：
在自动驾驶中，基于视觉的3D目标检测通过RGB图像识别和定位3D对象。然而，由于高标注成本和多样的户外场景，训练数据往往无法覆盖所有可能的测试场景，即分布外（OOD）问题。训练自由图像编辑提供了一种有希望的解决方案，通过数据增强来提高模型鲁棒性，而无需对预训练扩散模型进行任何修改。然而，基于反向的方法往往存在有限的效果和固有的不准确性，而近期基于校正流的方法在保持准确的3D几何结构方面存在困难。在本文中，我们提出了DriveFlow，一种基于预训练的文本到图像流模型的校正流适应方法，用于自动驾驶中的训练数据增强。基于频率分解，DriveFlow引入了两种策略来适应从文本条件速度导出的无噪声编辑路径。1）高频前景保持：DriveFlow引入高频对齐损失以保持精确的3D对象几何结构。2）双频背景优化：DriveFlow还对背景进行双频优化，平衡编辑灵活性和语义一致性。全面的实验验证了DriveFlow的有效性和效率，在所有OOD场景的所有类别上均展示了全面的性能提升。代码可在https://github.com/Hongbin98/DriveFlow获取。
英文摘要：
In autonomous driving, vision-centric 3D object detection recognizes and localizes 3D objects from RGB images. However, due to high annotation costs and diverse outdoor scenes, training data often fails to cover all possible test scenarios, known as the out-of-distribution (OOD) issue. Training-free image editing offers a promising solution for improving model robustness by training data enhancement without any modifications to pre-trained diffusion models. Nevertheless, inversion-based methods often suffer from limited effectiveness and inherent inaccuracies, while recent rectified-flow-based approaches struggle to preserve objects with accurate 3D geometry. In this paper, we propose DriveFlow, a Rectified Flow Adaptation method for training data enhancement in autonomous driving based on pre-trained Text-to-Image flow models. Based on frequency decomposition, DriveFlow introduces two strategies to adapt noise-free editing paths derived from text-conditioned velocities. 1) High-Frequency Foreground Preservation: DriveFlow incorporates a high-frequency alignment loss for foreground to maintain precise 3D object geometry. 2) Dual-Frequency Background Optimization: DriveFlow also conducts dual-frequency optimization for background, balancing editing flexibility and semantic consistency. Comprehensive experiments validate the effectiveness and efficiency of DriveFlow, demonstrating comprehensive performance improvements on all categories across OOD scenarios. Code is available at https://github.com/Hongbin98/DriveFlow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
