Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain.

This work studies black-box adversarial attacks against deep neural networks, where the attacker only has access to the query feedback from the target model. The current state-of-the-art (SOTA) query-efficient attacks usually combine transfer-based and query-based methods by utilizing the gradient or initializations of surrogate models. However, these strategies typically incur significant computational costs and require a large number of queries during the attack process. In this paper, we propose a novel query-efficient method for generating black-box adversarial perturbations, named Vector Quantization based Query-efficient Adversarial Perturbation generation (VQQAP). Specifically, we propose a Nucleus Sampling based Discretization Module (NSDM) to create diverse adversarial examples in the discrete latent space. To directly optimize the latent vector, we formulate the optimization problem as a direct preference optimization (DPO) problem, and iteratively solve this problem based on the target model feedback. Experimental evaluations demonstrate the effectiveness and efficiency of our method.