  Neural Radiance Fields (NeRF) have been widely adopted as practical and
versatile representations for 3D scenes, facilitating various downstream tasks.
However, different architectures, including the plain Multi-Layer Perceptron
(MLP), Tensors, low-rank Tensors, Hashtables, and their combinations, entail
distinct trade-offs. For instance, representations based on Hashtables enable
faster rendering but lack clear geometric meaning, thereby posing challenges
for spatial-relation-aware editing. To address this limitation and maximize the
potential of each architecture, we propose Progressive Volume Distillation with
Active Learning (PVD-AL), a systematic distillation method that enables
any-to-any conversion between diverse architectures. PVD-AL decomposes each
structure into two parts and progressively performs distillation from shallower
to deeper volume representation, leveraging effective information retrieved
from the rendering process. Additionally, a three-level active learning
technique provides continuous feedback from teacher to student during the
distillation process, achieving high-performance outcomes. Experimental
evidence showcases the effectiveness of our method across multiple benchmark
datasets. For instance, PVD-AL can distill an MLP-based model from a
Hashtables-based model at a 10~20X faster speed and 0.8dB~2dB higher PSNR than
training the MLP-based model from scratch. Moreover, PVD-AL permits the fusion
of diverse features among distinct structures, enabling models with multiple
editing properties and providing a more efficient model to meet real-time
requirements like mobile devices. Project website: https://sk-fun.fun/PVD-AL.
