Compact Neural Graphics Primitives with Learned Hash Probing

Abstract: Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid. However, existing feature grids either come with a large memory footprint (dense or factorized grids, trees, and hash tables) or slow performance (index learning and vector quantization). In this paper, we show that a hash table with learned probes has neither disadvantage, resulting in a favorable combination of size and speed. Inference is faster than unprobed hash tables at equal quality while training is only 1.2-2.6x slower, significantly outperforming prior index learning approaches. We arrive at this formulation by casting all feature grids into a common framework: they each correspond to a lookup function that indexes into a table of feature vectors. In this framework, the lookup functions of existing data structures can be combined by simple arithmetic combinations of their indices, resulting in Pareto optimal compression and speed.

• The paper demonstrates that Compact NGP significantly improves storage efficiency and inference speed by blending hash table lookups with learned probing.
• The method achieves a competitive quality-size trade-off on image datasets, markedly reducing memory footprints and computational costs.
• The technique paves the way for advanced multimedia applications, including texture compression in gaming and real-time rendering.

Introduction

The paper introduces an innovative approach to representing and compressing multimedia content across different formats, including images and volume data used in immersive experiences. It seeks to address the ever-growing demand for higher fidelity content, a challenge that current multimedia formats struggle to meet efficiently.

Current Compression Techniques

Lossy compression is a common strategy for reducing multimedia file sizes, generally involving steps such as transform coding, quantization, and entropy coding. However, the methods commonly used are often tailored for specific multimedia types and may not be well-suited for the latest high-dimensional formats such as volumetric video, requiring a more versatile approach.

Neural Graphic Primitives (NGP)

NGPs have emerged as a flexible solution capable of representing various data types and rendering novel views in applications ranging from image generation to light caching. These models typically use trained feature grids composed of latent embeddings, decoded by a neural network. While promising, these feature grids pose challenges due to either their large memory footprint or slow performance.

Compact NGP Methodology

The core contribution of the paper is the Compact Neural Graphics Primitives (Compact NGP), which improves storage efficiency and accelerates data access by blending traditional hash table speed with the compactness of learned probing techniques. This is achieved by managing lookup functions that index into a compact feature codebook, a technique that combines spatial hashing with learning indices. Reducing the feature codebook size and increasing cache utilization, the Compact NGP demonstrates improved performance during both training and inference as well as notable compression rates.

Performance and Results

The methodology proposed shows impactful results when tested against established benchmarks. On image datasets, the Compact NGP exhibits a competitive quality-size trade-off, especially noticeable in high-quality targets where existing methods struggle. Additionally, the storage cost is managed more efficiently as the representation becomes increasingly dominated by integer parameters rather than floating-point ones.

Future Work and Applications

Looking forward, the technique outlined in the paper has potential applications beyond what has been demonstrated. These include, but are not limited to, video game texture compression, real-time renderers handling texture compression, and live-streaming of high-dimensional multimedia formats. The authors suggest that future research into data-adaptive float quantization or integer entropy minimization could yield even more effective compression strategies.

Conclusion

The paper encapsulates the authors' efforts in proposing a versatile compression scheme via Compact NGP that not only addresses the storage and computational challenges of modern multimedia representation but is also capable of adapting to a wide range of multimedia types and quality requirements. This new approach is poised to play a significant role in the development and distribution of next-generation digital content.