Neural radiance fields (NeRFs) have emerged as an effective method for novel-view synthesis and 3D scene reconstruction. However, conventional training methods require access to all training views during scene optimization. This assumption may be prohibitive in continual learning scenarios, where new data is acquired in a sequential manner and a continuous update of the NeRF is desired, as in automotive or remote sensing applications. When naively trained in such a continual setting, traditional scene representation frameworks suffer from catastrophic forgetting, where previously learned knowledge is corrupted after training on new data. Prior works in alleviating forgetting with NeRFs suffer from low reconstruction quality and high latency, making them impractical for real-world application. We propose a continual learning framework for training NeRFs that leverages replay-based methods combined with a hybrid explicit--implicit scene representation. Our method outperforms previous methods in reconstruction quality when trained in a continual setting, while having the additional benefit of being an order of magnitude faster.

翻译：神经辐射场（NeRFs）已成为新颖视角合成和三维场景重建的有效方法。然而，传统的训练方法在场景优化过程中要求访问所有训练视图。这一假设在连续学习场景中可能难以实现，例如在自动驾驶或遥感应用中，新数据会按顺序获取，且需要不断更新NeRF。若在连续学习设置下简单地进行训练，传统场景表示框架会遭受灾难性遗忘问题，即先前学习的知识在新数据训练后被破坏。先前缓解NeRF遗忘问题的工作存在重建质量低和延迟高的问题，使其难以实际应用。我们提出了一种用于训练NeRF的连续学习框架，该方法结合了基于回放的策略与混合显式-隐式场景表示。在连续学习设置下训练时，我们的方法在重建质量上优于先前方法，同时具有快一个数量级的额外优势。