In this paper, we tackle the challenge of efficiently adapting NeRFs to real-world scene changes over time using a few new images while retaining the memory of ...

This work aims to tackle the challenge of efficiently adapting NeRFs to real-world scene changes in a continual learning setting.

In this paper, we tackle the challenge of efficiently adapting. NeRFs to real-world scene changes over time using a few new images while re- taining the memory ...

Aug 28, 2023 · We propose a new dataset, World Across Time (WAT), consisting of scenes that change in appearance and geometry over time. We also propose a simple yet ...

May 30, 2024 · In this paper, we tackle the challenge of efficiently adapting NeRFs to real-world scene changes over time using a few new images while ...

We also propose a simple yet effective method,. CLNeRF, which introduces continual learning (CL) to Neu- ral Radiance Fields (NeRFs). CLNeRF combines gener-.

Mar 11, 2024 · In this paper, we tackle the challenge of efficiently adapting NeRFs to real-world scene changes over time using a few new images while ...

To facilitate future research on continual NeRF, we provide the code to run different continual learning methods on different NeRF datasets (including WAT).

2020. CL-NeRF: continual learning of neural radiance fields for evolving scene representation ... GO-NeRF: Generating Virtual Objects in Neural Radiance Fields.

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