Neural radiance fields (NeRF) have exhibited highly photorealistic rendering of novel views through per-scene optimization over a single 3D scene. With the growing popularity of NeRF and its variants, they have become ubiquitous and have been identified as efficient 3D resources. However, they are still far from being scalable since a separate model needs to be stored for each scene, and the training time increases linearly with every newly added scene. Surprisingly, the idea of encoding multiple 3D scenes into a single NeRF model is heavily under-explored. In this work, we propose a novel conditional-cum-continual framework, called $C^{3}$-NeRF, to accommodate multiple scenes into the parameters of a single neural radiance field. Unlike conventional approaches that leverage feature extractors and pre-trained priors for scene conditioning, we use simple pseudo-scene labels to model multiple scenes in NeRF. Interestingly, we observe the framework is also inherently continual (via generative replay) with minimal, if not no, forgetting of the previously learned scenes. Consequently, the proposed framework adapts to multiple new scenes without necessarily accessing the old data. Through extensive qualitative and quantitative evaluation using synthetic and real datasets, we demonstrate the inherent capacity of the NeRF model to accommodate multiple scenes with high-quality novel-view renderings without adding additional parameters. We provide implementation details and dynamic visualizations of our results in the supplementary file.

本研究针对现有神经辐射场（NeRF）在处理多个三维场景时所面临的模型存储和训练时间问题，提出了一种新颖的条件持续框架$C^{3}$-NeRF。该框架通过简单的伪场景标签来处理多个场景，展现出不需要额外参数而能生成高质量新视图渲染的潜力，同时有效防止之前学习场景的遗忘。

$C^{3}$-NeRF：通过条件持续神经辐射场建模多个场景