NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation

Sicheng Li, Hao Li, Yiyi Liao, Lu Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 21274-21283

The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis. As a kind of visual media for 3D scene representation compression with high rate-distortion performance is an eternal target. Motivated by advances in neural compression and neural field representation we propose NeRFCodec an end-to-end NeRF compression framework that integrates non-linear transform quantization and entropy coding for memory-efficient scene representation. Since training a non-linear transform directly on a large scale of NeRF feature planes is impractical we discover that pre-trained neural 2D image codec can be utilized for compressing the features when adding content-specific parameters. Specifically we reuse neural 2D image codec but modify its encoder and decoder heads while keeping the other parts of the pre-trained decoder frozen. This allows us to train the full pipeline via supervision of rendering loss and entropy loss yielding the rate-distortion balance by updating the content-specific parameters. At test time the bitstreams containing latent code feature decoder head and other side information are transmitted for communication. Experimental results demonstrate our method outperforms existing NeRF compression methods enabling high-quality novel view synthesis with a memory budget of 0.5 MB.