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.

利用 LiDAR 技术进行 3D 目标检测中，我们提出了双扰动优化的方法 (TTA-3OD) 来解决测试阶段适应性问题，通过最小化锐度来保证模型对数据细微变化具有弹性，从而增强了适应性过程的泛化性能。通过引入对抗扰动来更好地模拟嘈杂的测试环境，进一步捕捉测试点云的固有变异性。在三类迁移任务上进行的大量实验证明，所提方法显著超过了先前的最先进方法，在 Waymo 到 KITTI 的迁移任务中，在 3D 精确率 (AP) 上比最具竞争力的基准方法高 57.72％，接近完全监督方法的上限的 91％。

DPO: 三维物体检测中的测试时间适应性双扰动优化

DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D  Object Detection

Model generalization ability upon incrementally acquiring dynamically
updating knowledge from sequentially arriving tasks is crucial to tackle the
sensitivity-stability dilemma in Continual Learning (CL). Weight loss landscape
sharpness minimization seeking for flat minima lying in neighborhoods with
uniform low loss or smooth gradient is proven to be a strong training regime
improving model generalization compared with loss minimization based optimizer
like SGD. Yet only a few works have discussed this training regime for CL,
proving that dedicated designed zeroth-order sharpness optimizer can improve CL
performance. In this work, we propose a Continual Flatness (C-Flat) method
featuring a flatter loss landscape tailored for CL. C-Flat could be easily
called with only one line of code and is plug-and-play to any CL methods. A
general framework of C-Flat applied to all CL categories and a thorough
comparison with loss minima optimizer and flat minima based CL approaches is
presented in this paper, showing that our method can boost CL performance in
almost all cases. Code will be publicly available upon publication.

通过连续学习方法中的权重损失景观锐度最小化，本研究提出了一种适用于连续学习任务的 C-Flat 方法，可以在几乎所有情况下提高模型性能。