LiDAR-based 3D object detection is pivotal across many applications, yet the performance of such detection systems often degrades after deployment, especially when faced with unseen test point clouds originating from diverse locations or subjected to corruption. In this work, we introduce a new online adaptation framework for detectors named Model Synergy (MOS). Specifically, MOS dynamically assembles best-fit supermodels for each test batch from a bank of historical checkpoints, leveraging long-term knowledge to guide model updates without forgetting. The model assembly is directed by the proposed synergy weights (SW), employed for weighted averaging of the selected checkpoints to minimize redundancy in the composite supermodel. These weights are calculated by evaluating the similarity of predicted bounding boxes on test data and the feature independence among model pairs in the bank. To maintain an informative yet compact model bank, we pop out checkpoints with the lowest average SW scores and insert newly updated model weights. Our method was rigorously tested against prior test-time domain adaptation strategies on three datasets and under eight types of corruptions, demonstrating its superior adaptability to changing scenes and conditions. Remarkably, our approach achieved a 67.3% increase in performance in a complex "cross-corruption" scenario, which involves cross-dataset inconsistencies and real-world scene corruptions, providing a more realistic testbed of adaptation capabilities. The code is available at https://github.com/zhuoxiao-chen/MOS.

翻译：基于激光雷达的三维目标检测在许多应用中至关重要，但此类检测系统在部署后性能常出现下降，尤其是在面对来自不同地点或遭受扰动的未见测试点云时。本研究提出了一种名为模型协同（MOS）的新型在线自适应检测框架。具体而言，MOS从历史检查点库中为每个测试批次动态组装最优超模型，利用长期知识指导模型更新而不遗忘。模型组装由提出的协同权重（SW）引导，该权重用于对所选检查点进行加权平均，以最小化复合超模型中的冗余。这些权重通过评估测试数据上预测边界框的相似性以及模型库中模型对之间的特征独立性来计算。为保持模型库信息丰富且紧凑，我们移除平均SW得分最低的检查点，并插入新更新的模型权重。我们在三个数据集和八种扰动类型下，将本方法与先前的测试时域自适应策略进行了严格对比测试，证明了其对变化场景和条件的卓越适应能力。值得注意的是，在涉及跨数据集不一致性和真实场景扰动的复杂“交叉扰动”场景中，我们的方法实现了67.3%的性能提升，为自适应能力提供了更真实的测试平台。代码发布于https://github.com/zhuoxiao-chen/MOS。