Neural Feature Compression for Memory-Efficient Neural Radiance Field Representation

Core Concepts

NeRFCodec, an end-to-end compression framework that integrates non-linear transform, quantization, and entropy coding, achieves memory-efficient scene representation for plane-based hybrid NeRF.

Abstract

The paper introduces NeRFCodec, an end-to-end compression framework for plane-based hybrid Neural Radiance Fields (NeRF). The key idea is to leverage non-linear transform, quantization, and entropy coding to compress the feature planes in hybrid NeRF, enabling memory-efficient scene representation.
The main components of NeRFCodec are:

Content-adaptive feature encoder: The encoder is initialized from a pre-trained 2D image codec and fine-tuned for each scene to obtain the latent code.
Content-adaptive decoder head: The decoder backbone is reused from the pre-trained 2D codec, while the final decoder head is fine-tuned for each scene.
Feature compensation: A high-frequency residual compensation module is introduced to recover the lost high-frequency details during lossy compression.
Quantization and entropy coding: The latent code is quantized and entropy-coded to form the final bitstream for transmission.

The experiments show that NeRFCodec can represent a single scene using only 0.5 MB of memory while maintaining high-quality novel view synthesis, outperforming existing NeRF compression methods.

Stats

The paper reports the following key metrics:

NeRF-Synthetic dataset: NeRFCodec achieves 36.60 dB PSNR at 0.91 MB.
NSVF-Synthetic dataset: NeRFCodec achieves 36.21 dB PSNR at 0.46 MB.
Tanks&Temples dataset: NeRFCodec achieves 26.71 dB PSNR at 0.45 MB.

Quotes

"NeRFCodec, an end-to-end NeRF compression framework that integrates non-linear transform, quantization, and entropy coding for memory-efficient scene representation."
"We propose to re-use pre-trained neural 2D image codec with slight modification and fine-tune it to each scene individually via the supervision of rate-distortion loss."
"Our experimental results demonstrate that NeRFCodec pushes the frontier of the rate-distortion trade-off compared to existing NeRF compression methods."

Key Insights Distilled From

NeRFCodec

by Sicheng Li,H... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02185.pdf

Deeper Inquiries

How can the proposed NeRFCodec framework be extended to handle dynamic scenes or time-varying neural radiance fields

To extend the NeRFCodec framework to handle dynamic scenes or time-varying neural radiance fields, we can introduce temporal coherence into the compression process. This can be achieved by incorporating motion estimation techniques to track changes in the scene over time. By considering the temporal dimension, the framework can encode not only the spatial information but also the evolution of the scene over different frames. This would involve capturing the changes in radiance fields and feature planes across consecutive frames and compressing them efficiently. Additionally, the framework can leverage predictive coding methods to estimate future frames based on the current and past frames, reducing the amount of information that needs to be stored or transmitted.

What are the potential challenges in training a generalized neural feature codec that can effectively compress feature planes from diverse 3D scenes without the need for individual fine-tuning

Training a generalized neural feature codec that can effectively compress feature planes from diverse 3D scenes without the need for individual fine-tuning poses several challenges. One major challenge is the variability in scene complexity, structure, and content across different scenes. The codec needs to be robust enough to adapt to these variations without sacrificing compression performance. Additionally, ensuring that the codec generalizes well to unseen scenes requires a diverse and representative training dataset that covers a wide range of scene types. Balancing the trade-off between model complexity and generalization capacity is crucial, as a highly complex model may overfit to the training data, while a simpler model may lack the capacity to capture the intricacies of diverse scenes.

Can the high-frequency residual compensation module be further improved to better preserve perceptual details in the reconstructed scenes

The high-frequency residual compensation module can be further improved to better preserve perceptual details in the reconstructed scenes by enhancing the modeling of high-frequency components. One approach could be to incorporate more sophisticated techniques such as wavelet transforms or multi-scale analysis to capture fine details effectively. By analyzing the frequency spectrum of the residual components and adapting the compensation method based on the energy distribution in different frequency bands, the module can prioritize preserving important high-frequency information while efficiently compressing the data. Additionally, exploring advanced loss functions that specifically target the preservation of high-frequency details, such as perceptual loss or adversarial training, can further enhance the fidelity of the reconstructed scenes.