HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression
- URL: http://arxiv.org/abs/2411.18473v1
- Date: Wed, 27 Nov 2024 16:08:59 GMT
- Title: HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression
- Authors: Lei Liu, Zhenghao Chen, Dong Xu,
- Abstract summary: 3D Gaussian Splatting (3DGS) is popular for 3D modeling and image rendering, but this creates big challenges in data storage and transmission.
We propose a hybrid entropy model for 3DGS data compression, which comprises two primary components, a hyperprior network and an autoregressive network.
Our method achieves about 40% average reduction in size while maintaining the rendering quality over our baseline method and achieving state-of-the-art compression results.
- Score: 23.015728369640136
- License:
- Abstract: Fast progress in 3D Gaussian Splatting (3DGS) has made 3D Gaussians popular for 3D modeling and image rendering, but this creates big challenges in data storage and transmission. To obtain a highly compact 3DGS representation, we propose a hybrid entropy model for Gaussian Splatting (HEMGS) data compression, which comprises two primary components, a hyperprior network and an autoregressive network. To effectively reduce structural redundancy across attributes, we apply a progressive coding algorithm to generate hyperprior features, in which we use previously compressed attributes and location as prior information. In particular, to better extract the location features from these compressed attributes, we adopt a domain-aware and instance-aware architecture to respectively capture domain-aware structural relations without additional storage costs and reveal scene-specific features through MLPs. Additionally, to reduce redundancy within each attribute, we leverage relationships between neighboring compressed elements within the attributes through an autoregressive network. Given its unique structure, we propose an adaptive context coding algorithm with flexible receptive fields to effectively capture adjacent compressed elements. Overall, we integrate our HEMGS into an end-to-end optimized 3DGS compression framework and the extensive experimental results on four benchmarks indicate that our method achieves about 40\% average reduction in size while maintaining the rendering quality over our baseline method and achieving state-of-the-art compression results.
Related papers
- HAC++: Towards 100X Compression of 3D Gaussian Splatting [55.6351304553003]
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity.
However, the sparse and unorganized nature of the point cloud of Gaussians (or anchors in our paper) presents challenges for compression.
We propose HAC++, which leverages the relationships between unorganized anchors and a structured hash grid, utilizing their mutual information for context modeling.
arXiv Detail & Related papers (2025-01-21T16:23:05Z) - Locality-aware Gaussian Compression for Fast and High-quality Rendering [37.16956462469969]
We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of scenes.
We first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effectively encodes locally-coherent Gaussian attributes.
arXiv Detail & Related papers (2025-01-10T07:19:41Z) - A Hierarchical Compression Technique for 3D Gaussian Splatting Compression [23.785131033155924]
3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis.
Current 3D GS compression research primarily focuses on developing more compact scene representations.
We propose a Hierarchical GS Compression (HGSC) technique to address this gap.
arXiv Detail & Related papers (2024-11-11T13:34:24Z) - Fast Feedforward 3D Gaussian Splatting Compression [55.149325473447384]
3D Gaussian Splatting (FCGS) is an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass.
FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods.
arXiv Detail & Related papers (2024-10-10T15:13:08Z) - Implicit Gaussian Splatting with Efficient Multi-Level Tri-Plane Representation [45.582869951581785]
Implicit Gaussian Splatting (IGS) is an innovative hybrid model that integrates explicit point clouds with implicit feature embeddings.
We introduce a level-based progressive training scheme, which incorporates explicit spatial regularization.
Our algorithm can deliver high-quality rendering using only a few MBs, effectively balancing storage efficiency and rendering fidelity.
arXiv Detail & Related papers (2024-08-19T14:34:17Z) - ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model [77.71796503321632]
We introduce a context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS.
Our work pioneers the context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS and 15 times compared to the most recent state-of-the-art work Scaffold-GS.
arXiv Detail & Related papers (2024-05-31T09:23:39Z) - SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene.
SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
arXiv Detail & Related papers (2024-04-29T23:26:30Z) - CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting [68.94594215660473]
We propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS)
We exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms.
Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality.
arXiv Detail & Related papers (2024-04-15T04:50:39Z) - HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression [55.6351304553003]
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis.
We propose a Hash-grid Assisted Context (HAC) framework for highly compact 3DGS representation.
Our work is the pioneer to explore context-based compression for 3DGS representation, resulting in a remarkable size reduction of over $75times$ compared to vanilla 3DGS.
arXiv Detail & Related papers (2024-03-21T16:28:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.