Tri-AutoAug: Single Domain Generalization for Bird's-Eye-View 3D Object Detection Through Pixel-2D-3D Features

Abstract

With the increasing popularity of autonomous driving based on the Bird's-Eye-View (BEV) representation, improving the generalization of such detection models is key for safe real-world applications. However, a realistic yet challenging scenario: Single Domain Generalization (SDG) for BEV, is still under-explored. A key ingredient for SDG is to increase data diversity via common image augmentation or adversarial data generation first. However, common image-level augmentation is not sufficient enough to ensure domain diversity in most part of latent space. The adversarial generation has the problem of unstable training or mode collapsing as well. To address these limitations, we present Tri-level Automatic Augmentation (Tri-AutoAug), a simple yet effective method to enlarge the diversity and quantity of data from image and 2D features and facilitate the model to learn more domain-invariant features in BEV space. Besides, Tri-AutoAug can automatically learn augmentation strategies to avoid spending too much time manually adjusting hyperparameters and maximize the benefit of Tri-level Augmentation. To the best of our knowledge, this is the first study to explore automatic augmentation for SDG BEV. Extensive experiments on NuScenes-C including eight testing domains have demonstrated that our approach can achieve the best performance across various domain generalization methods. More importantly, we evaluate the proposed method in real-world autonomous driving scenarios. Tri-AutoAug improves the out-of-distribution (ood) performance by 8.54% (mAP), which demonstrates that Tri-AutoAug provides a practical and feasible solution for the applications of 3D detectors in the real world. The code is available at https://github.com/ClaireTunlTri-AutoAug.