SCARF: Scalable Continual Learning Framework for Memory-efficient Multiple Neural Radiance Fields

TL;DR

This paper presents SCARF, a scalable continual learning framework for multiple Neural Radiance Fields that efficiently manages memory and prevents forgetting, enabling high-quality rendering of multiple scenes with low storage costs.

Contribution

SCARF introduces a novel weight matrix representation and an uncertain surface knowledge distillation strategy for efficient, continual learning of multiple NeRF scenes.

Findings

Achieves state-of-the-art rendering quality on multiple datasets.

Significantly reduces memory requirements for multiple scene NeRFs.

Effectively prevents catastrophic forgetting during continual learning.

Abstract

This paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build on Neural Radiance Fields (NeRF), which uses multi-layer perceptron to model the density and radiance field of a scene as the implicit function. While NeRF and its extensions have shown a powerful capability of rendering photo-realistic novel views in a single 3D scene, managing these growing 3D NeRF assets efficiently is a new scientific problem. Very few works focus on the efficient representation or continuous learning capability of multiple scenes, which is crucial for the practical applications of NeRF. To achieve these goals, our key idea is to represent multiple scenes as the linear combination of a cross-scene weight matrix and a set of scene-specific weight matrices generated from a global parameter generator. Furthermore, we propose an uncertain surface knowledge distillation strategy to transfer the radiance field knowledge of previous scenes to the new model. Representing multiple 3D scenes with such weight matrices significantly reduces memory requirements. At the same time, the uncertain surface distillation strategy greatly overcomes the catastrophic forgetting problem and maintains the photo-realistic rendering quality of previous scenes. Experiments show that the proposed approach achieves state-of-the-art rendering quality of continual learning NeRF on NeRF-Synthetic, LLFF, and TanksAndTemples datasets while preserving extra low storage cost.