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.

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