PLS: UNSUPERVISED DOMAIN ADAPTATION FOR 3D OBJECT DETECTION VIA PSEUDO-LABEL SIZES

3D object detection has gained increasing attention in modern autonomous driving systems. However, the performance of the detector significantly degrades during cross-domain deployment due to domain shift. The detector is inevitably biased towards its training dataset when employed on a target dataset, particularly towards object sizes. State-of-the-art unsupervised domain adaptation approaches explicitly address the variation in object sizes by appropriately scaling the source data. However, such methods require additional target domain statistics information, which contradicts the original unsupervised assumption. In this work, we present PLS, a novel unsupervised domain adaptation method for 3D object detection to overcome the object sizes bias via Pseudo-Label Sizes, which utilizes only source domain annotations. PLS alternates between generating high-quality pseudo-label sizes through the detector and model training with the pseudo-label sizes to scale and augment the source data. This iterative process enables the detector to be trained with augmented data that resembles the target domain sizes, thereby improving the performance of detector in cross-domain scenarios. Our experimental results show the outstanding performance of our PLS in various scenarios. In addition, PLS is a plug-and-play module that can be used to directly replace existing weakly-supervised scaling methods. Experimental results show that existing excellent architectures with PLS are able to achieve better performance, and making them completely unsupervised.