CodecNeRF: Toward Fast Encoding and Decoding, Compact, and High-quality
  Novel-view Synthesis
Gyeongjin Kang Younggeun Lee Eunbyung Park
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.