Compressing Volumetric Radiance Fields to 1 MB (2211.16386v1)

Abstract: Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering. However, these methods typically require a tremendous storage overhead, costing up to hundreds of megabytes of disk space and runtime memory for a single scene. We address this issue in this paper by introducing a simple yet effective framework, called vector quantized radiance fields (VQRF), for compressing these volume-grid-based radiance fields. We first present a robust and adaptive metric for estimating redundancy in grid models and performing voxel pruning by better exploring intermediate outputs of volumetric rendering. A trainable vector quantization is further proposed to improve the compactness of grid models. In combination with an efficient joint tuning strategy and post-processing, our method can achieve a compression ratio of 100$\times$ by reducing the overall model size to 1 MB with negligible loss on visual quality. Extensive experiments demonstrate that the proposed framework is capable of achieving unrivaled performance and well generalization across multiple methods with distinct volumetric structures, facilitating the wide use of volumetric radiance fields methods in real-world applications. Code Available at \url{https://github.com/AlgoHunt/VQRF}

• The paper presents VQRF, integrating voxel pruning and vector quantization to drastically reduce storage requirements in volumetric radiance fields.
• It achieves a 100× compression ratio, maintaining high PSNR and SSIM values across multiple datasets with negligible quality loss.
• Joint tuning and post-processing techniques further optimize quality, enabling practical real-time 3D scene rendering for VR/AR applications.

A Comprehensive Analysis of "Compressing Volumetric Radiance Fields to 1 MB"

The paper "Compressing Volumetric Radiance Fields to 1 MB" addresses the substantial storage overhead associated with novel volumetric methods for Neural Radiance Fields (NeRF). Specifically, the work focuses on advancing the efficiency of volumetric grids employed in radiance fields approaches such as Plenoxels and Direct Voxel Grid Optimization (DVGO), which accommodate fast training convergence and real-time rendering but demand significant storage capacity, often reaching hundreds of megabytes for a single scene.

Key Contributions

The authors present a method known as Vector Quantized Radiance Fields (VQRF), which adeptly compresses volume-grid-based radiance fields while retaining visual fidelity. The major innovations of the framework include:

1. Voxel Pruning Methodology: The framework implements a robust redundancy estimation metric to intelligently prune voxels. This method utilizes intermediate outputs from volumetric rendering to identify and remove voxels of lower importance, optimizing storage usage and preserving critical visual details.
2. Vector Quantization: The paper introduces a trainable vector quantization process which enhances the compactness of grid models by effectively clustering voxel features into a codebook, allowing multiple voxels to be represented by fewer code vectors without compromising quality.
3. Joint Tuning and Post-Processing: VQRF employs an efficient joint tuning mechanism post-compression to reconcile any loss in rendering quality with the original model through fine-tuning, complemented by weight quantization and entropy encoding in the post-processing phase, achieving substantial storage reductions.

Experimental Validation

Substantial experimental validation underscores the efficacy of the VQRF framework. The authors report a compression ratio of 100×, successfully reducing the model size to approximately 1 MB with negligible deterioration in rendering quality. Specifically, VQRF demonstrates competency across various methods with different volumetric configurations, achieving competitive results without the prohibitive storage costs typically incurred.

Quantitative evaluations on multiple datasets, including Synthetic-NeRF, LLFF, and Tanks and Temples, reveal the VQRF’s potential to maintain high PSNR and SSIM values while significantly reducing the storage footprint. The qualitative assessments corroborate the numerical results, showing minimal visual artifacts post-compression, thereby validating the visual quality retention claims.

Implications on Future Research

The implications of this research are multifaceted, impacting both theoretical exploration and practical application within the field of AI, especially in domains related to 3D scene rendering and virtual environments where resource constraints are a concern. By mitigating storage inefficiencies, VQRF could enhance the practicality of real-time deployments of volumetric radiance fields in Virtual and Augmented Reality settings, thus widening the accessibility and usability of NeRF derivatives.

Additionally, the principles of efficient data representation through vector quantization and voxel redundancy exploitation proposed in this paper could spark further advancements in compressive techniques for other high-dimensional data representations, potentially extending beyond the field of 3D scene processing to broader applications in machine learning.

Conclusion

In conclusion, the VQRF framework presents a significant step forward in overcoming the storage challenges faced by current state-of-the-art volumetric radiance fields for NeRF. By elegantly combining voxel pruning, vector quantization, and joint tuning with post-processing strategies, this work sets a new benchmark for efficient and effective compression of complex 3D scene representations. Future inquiries and developments might explore heightened compression rates or adaptations to novel data structures, reinforcing VQRF's standing in the annals of computational imaging research.