  LiDAR-based 3D object detection has seen impressive advances in recent times.
However, deploying trained 3D detectors in the real world often yields
unsatisfactory performance when the distribution of the test data significantly
deviates from the training data due to different weather conditions, object
sizes, \textit{etc}. A key factor in this performance degradation is the
diminished generalizability of pre-trained models, which creates a sharp loss
landscape during training. Such sharpness, when encountered during testing, can
precipitate significant performance declines, even with minor data variations.
To address the aforementioned challenges, we propose \textbf{dual-perturbation
optimization (DPO)} for \textbf{\underline{T}est-\underline{t}ime
\underline{A}daptation in \underline{3}D \underline{O}bject
\underline{D}etection (TTA-3OD)}. We minimize the sharpness to cultivate a flat
loss landscape to ensure model resiliency to minor data variations, thereby
enhancing the generalization of the adaptation process. To fully capture the
inherent variability of the test point clouds, we further introduce adversarial
perturbation to the input BEV features to better simulate the noisy test
environment. As the dual perturbation strategy relies on trustworthy
supervision signals, we utilize a reliable Hungarian matcher to filter out
pseudo-labels sensitive to perturbations. Additionally, we introduce early
Hungarian cutoff to avoid error accumulation from incorrect pseudo-labels by
halting the adaptation process. Extensive experiments across three types of
transfer tasks demonstrate that the proposed DPO significantly surpasses
previous state-of-the-art approaches, specifically on Waymo $\rightarrow$
KITTI, outperforming the most competitive baseline by 57.72\% in
$\text{AP}_\text{3D}$ and reaching 91\% of the fully supervised upper bound.
