LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D.
翻译:以 LiDAR 为基础的 3D 对象探测是高级自主驱动系统的一项不可或缺的任务。 尽管高级 3D 探测器已经实现了令人印象深刻的检测结果, 但是在面临隐蔽域, 如不同的 3DAR 配置、 不同的城市和天气条件时, 3D 对象检测结果会发生显著的性能退化。 主流方法往往通过利用不受监督的域适应( UDA) 技术来解决这些挑战。 然而, 这些 UDA 解决方案只是当有严格的域变换, 例如从 Waymo( 64- 直径) 到 nuSceenScenes (32- beam) 时, 才能产生无法令人满意的 3D 3D 的检测结果。 为了解决这个问题, 我们为 3D 目标检测( SSSSSD3D3D) 展示了新型超超超超超超超超超超超超超超超超超超超超多的多域适应方法, 并且能够大大改善适应性能性能。 特别是, 我们的SDD3DDDDDD 包括一个内部适应性内部适应阶段, 和普通的内, 。 通过模型- mal- deal- dreabreablearaldaldaldaldald, 我们的第二域域域域域列, 在模型和Sl 学习中, 上, 。