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 this https URL

基于 LiDAR 的 3D 物体检测中，引入了一种名为模型协同的在线自适应框架，通过动态组装最佳的超级模型来指导模型更新，同时在复合超级模型中最小化冗余。该方法在多个数据集和不同类型的数据损坏下进行了全面测试，并在复杂的 “跨数据集” 场景中表现出优越的适应能力。