Neural Radiance Fields (NeRF) have achieved huge success in effectively
capturing and representing 3D objects and scenes. However, several factors have
impeded its further proliferation as next-generation 3D media. To establish a
ubiquitous presence in everyday media formats, such as images and videos, it is
imperative to devise a solution that effectively fulfills three key objectives:
fast encoding and decoding time, compact model sizes, and high-quality
renderings. Despite significant advancements, a comprehensive algorithm that
adequately addresses all objectives has yet to be fully realized. In this work,
we present CodecNeRF, a neural codec for NeRF representations, consisting of a
novel encoder and decoder architecture that can generate a NeRF representation
in a single forward pass. Furthermore, inspired by the recent
parameter-efficient finetuning approaches, we develop a novel finetuning method
to efficiently adapt the generated NeRF representations to a new test instance,
leading to high-quality image renderings and compact code sizes. The proposed
CodecNeRF, a newly suggested encoding-decoding-finetuning pipeline for NeRF,
achieved unprecedented compression performance of more than 150x and 20x
reduction in encoding time while maintaining (or improving) the image quality
on widely used 3D object datasets, such as ShapeNet and Objaverse.

通过提出一种新的编码 - 解码 - 微调流程，CodecNeRF 在保持 (或改善) 图像质量的同时，在常用的 3D 对象数据集（如 ShapeNet 和 Objaverse）上实现了超过 150 倍的压缩性能和 20 倍的编码时间缩短。