Accurate 3D object detection in all weather conditions remains a key challenge to enable the widespread deployment of autonomous vehicles, as most work to date has been performed on clear weather data. In order to generalize to adverse weather conditions, supervised methods perform best if trained from scratch on all weather data instead of finetuning a model pretrained on clear weather data. Training from scratch on all data will eventually become computationally infeasible and expensive as datasets continue to grow and encompass the full extent of possible weather conditions. On the other hand, naive finetuning on data from a different weather domain can result in catastrophic forgetting of the previously learned domain. Inspired by the success of replay-based continual learning methods, we propose Gradient-based Maximally Interfered Retrieval (GMIR), a gradient based sampling strategy for replay. During finetuning, GMIR periodically retrieves samples from the previous domain dataset whose gradient vectors show maximal interference with the gradient vector of the current update. Our 3D object detection experiments on the SeeingThroughFog (STF) dataset show that GMIR not only overcomes forgetting but also offers competitive performance compared to scratch training on all data with a 46.25% reduction in total training time.

翻译：全天候条件下的精确三维目标检测仍是实现自动驾驶车辆广泛部署的关键挑战，因为迄今大多数研究均基于晴好天气数据开展。为泛化至恶劣天气条件，与在晴好天气数据预训练模型上进行微调相比，全监督方法在所有天气数据上从头训练效果最佳。然而，随着数据集持续扩展并涵盖全维度天气条件，对所有数据从头训练将逐渐面临计算不可行性与高成本问题。另一方面，对异源天气域数据进行简单微调可能导致先前学习域的灾难性遗忘。受基于回放的持续学习方法的启发，我们提出基于梯度的最大干扰检索——一种基于梯度的回放采样策略。在微调过程中，GMIR周期性从先前领域数据集中检索梯度向量与当前更新梯度向量呈现最大干扰的样本。我们在SeeingThroughFog数据集上的三维目标检测实验表明，GMIR不仅克服了灾难性遗忘，而且相较于全数据从头训练方法，在总训练耗时降低46.25%的同时展现出具有竞争力的性能表现。