  Hinged on the representation power of neural networks, neural radiance fields
(NeRF) have recently emerged as one of the promising and widely applicable
methods for 3D object and scene representation. However, NeRF faces challenges
in practical applications, such as large-scale scenes and edge devices with a
limited amount of memory, where data needs to be processed sequentially. Under
such incremental learning scenarios, neural networks are known to suffer
catastrophic forgetting: easily forgetting previously seen data after training
with new data. We observe that previous incremental learning algorithms are
limited by either low performance or memory scalability issues. As such, we
develop a Memory-Efficient Incremental Learning algorithm for NeRF (MEIL-NeRF).
MEIL-NeRF takes inspiration from NeRF itself in that a neural network can serve
as a memory that provides the pixel RGB values, given rays as queries. Upon the
motivation, our framework learns which rays to query NeRF to extract previous
pixel values. The extracted pixel values are then used to train NeRF in a
self-distillation manner to prevent catastrophic forgetting. As a result,
MEIL-NeRF demonstrates constant memory consumption and competitive performance.
