HAC++: Towards 100X Compression of 3D Gaussian Splatting (2501.12255v4)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial Gaussians and their associated attributes necessitate effective compression techniques. Nevertheless, the sparse and unorganized nature of the point cloud of Gaussians (or anchors in our paper) presents challenges for compression. To achieve a compact size, we propose HAC++, which leverages the relationships between unorganized anchors and a structured hash grid, utilizing their mutual information for context modeling. Additionally, HAC++ captures intra-anchor contextual relationships to further enhance compression performance. To facilitate entropy coding, we utilize Gaussian distributions to precisely estimate the probability of each quantized attribute, where an adaptive quantization module is proposed to enable high-precision quantization of these attributes for improved fidelity restoration. Moreover, we incorporate an adaptive masking strategy to eliminate invalid Gaussians and anchors. Overall, HAC++ achieves a remarkable size reduction of over 100X compared to vanilla 3DGS when averaged on all datasets, while simultaneously improving fidelity. It also delivers more than 20X size reduction compared to Scaffold-GS. Our code is available at https://github.com/YihangChen-ee/HAC-plus.

• The paper introduces the HAC++ framework, which leverages structured hash grids and intra-anchor contextual relationships to model spatial dependencies and achieve significant compression of 3D Gaussian splatting data.
• Adaptive Quantization and masking strategies within HAC++ dynamically refine attribute quantization and eliminate superfluous Gaussians and anchors, further optimizing compression while preserving fidelity.
• HAC++ achieves a remarkable average size reduction of over 100 times compared to original 3DGS, retaining high fidelity across datasets and making large-scale deployments feasible.

Compression of 3D Gaussian Splatting Using HAC++

The paper "HAC++: Towards 100X Compression of 3D Gaussian Splatting" introduces an innovative framework for compressing 3D Gaussian splatting (3DGS) representations, with the aim of achieving substantial data size reductions while maintaining or enhancing fidelity. The proposed method, HAC++, builds upon the previous work Scaffold-GS and advocates for the integration of structured hash grids with sparse, unorganized Gaussian data to uncover and leverage mutual redundancies for effective compression.

3DGS is recognized for its rapid rendering speed and high visual fidelity in novel view synthesis. However, its application is hindered by the extensive data storage demands due to the millions of Gaussian primitives involved. The traditional methods either overlook the structural relations or are inefficient for large-scale scenes, thus creating a demand for a better compression approach.

Main Contributions

1. Hash-Grid Assisted Context (HAC) Framework: HAC++ employs a structured hash grid to model and compress 3D Gaussians. The innovation lies in interpolating hash grid features using anchor locations, which help predict and compress anchor attributes' probability distributions. Differentiating from other methods, this technique efficiently captures the spatial dependencies within the data.
2. Intra-Anchor Contextual Relationships: Beyond the use of hash grids, HAC++ introduces an intra-anchor context model that exploits the internal redundancies within anchors. This multi-layer approach enables improved probability estimation and thus, reduced entropy in data representation, assisting effective compression without compromising the rendering quality.
3. Adaptive Quantization and Masking Strategies: The framework utilizes an Adaptive Quantization Module (AQM) that dynamically refines attribute quantization steps to preserve fidelity. Additionally, HAC++ integrates an adaptive masking strategy for eliminating superfluous Gaussians and anchors, further optimizing compression by reducing unnecessary computations.
4. Rate-Distortion Performance: The paper reports a remarkable average size reduction of more than 100 times compared to the original 3DGS and retains high fidelity across multiple datasets. This is a notable achievement given the inherent complexity and data density of 3D Gaussian splatting representations.

Implications and Future Directions

HAC++ fundamentally shifts the approach to handling the data-heavy nature of 3DGS, making it feasible for large-scale deployments such as city-scale scenes or intricate virtual environments. The blend of sophisticated context modeling with adaptive quantization and masking suggests that future research could yield even more efficient models by integrating learned hierarchical relations, potentially reducing time complexity in both training and inference phases.

Additionally, the success of coupling hash grids with anchor-based Gaussian data presents an opportunity to explore hybrid models across other domains of data that involve spatial and structural complexities. As machine learning and computer vision systems evolve to tackle more complex scenes, the methodologies proposed in HAC++ could serve as a blueprint for achieving scalable, efficient, and high-fidelity data compression.

In conclusion, HAC++ makes a significant contribution to the domain of 3D scene representation, offering a comprehensively structured approach to large data compression while enhancing the visual fidelity essential for real-time rendering applications. The methodology not only addresses current needs but also sets the stage for future enhancements in data compression techniques for complex volumetric datasets.