SA-3DGS: A Self-Adaptive Compression Method for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2508.03017v1
• Date: Tue, 05 Aug 2025 02:55:47 GMT
• Title: SA-3DGS: A Self-Adaptive Compression Method for 3D Gaussian Splatting
• Authors: Liheng Zhang, Weihao Yu, Zubo Lu, Haozhi Gu, Jin Huang,
• Abstract summary: Recent advancements in 3D Gaussian Splatting have enhanced efficient and high-quality novel view synthesis.<n> representing scenes requires a large number of Gaussian points, leading to high storage demands and limiting practical deployment.<n>We propose SA-3DGS, a method that significantly reduces storage costs while maintaining rendering quality.
• Score: 17.200194808602063
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recent advancements in 3D Gaussian Splatting have enhanced efficient and high-quality novel view synthesis. However, representing scenes requires a large number of Gaussian points, leading to high storage demands and limiting practical deployment. The latest methods facilitate the compression of Gaussian models but struggle to identify truly insignificant Gaussian points in the scene, leading to a decline in subsequent Gaussian pruning, compression quality, and rendering performance. To address this issue, we propose SA-3DGS, a method that significantly reduces storage costs while maintaining rendering quality. SA-3DGS learns an importance score to automatically identify the least significant Gaussians in scene reconstruction, thereby enabling effective pruning and redundancy reduction. Next, the importance-aware clustering module compresses Gaussians attributes more accurately into the codebook, improving the codebook's expressive capability while reducing model size. Finally, the codebook repair module leverages contextual scene information to repair the codebook, thereby recovering the original Gaussian point attributes and mitigating the degradation in rendering quality caused by information loss. Experimental results on several benchmark datasets show that our method achieves up to 66x compression while maintaining or even improving rendering quality. The proposed Gaussian pruning approach is not only adaptable to but also improves other pruning-based methods (e.g., LightGaussian), showcasing excellent performance and strong generalization ability.

Related papers

• ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes [81.48624894781257]
  3D Gaussian Splatting (3DGS) has made significant strides in novel view synthesis but is limited by the substantial number of Gaussian primitives required.<n>Recent methods address this issue by compressing the storage size of densified Gaussians, yet fail to preserve rendering quality and efficiency.<n>We propose ProtoGS to learn Gaussian prototypes to represent Gaussian primitives, significantly reducing the total Gaussian amount without sacrificing visual quality.
  arXiv  Detail & Related papers  (2025-03-21T18:55:14Z)
• Optimized Minimal 3D Gaussian Splatting [10.344911588975199]
  3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering.<n> representing 3D scenes with numerous explicit Gaussian primitives imposes significant storage and memory overhead.<n>We propose a compact and precise attribute representation that efficiently captures both continuity and irregularity among primitives.
  arXiv  Detail & Related papers  (2025-03-21T07:41:45Z)
• PCGS: Progressive Compression of 3D Gaussian Splatting [55.149325473447384]
  We propose PCGS (Progressive Compression of 3D Gaussian Splatting), which adaptively controls both the quantity and quality of Gaussians.<n>Overall, PCGS achieves progressivity while maintaining compression performance comparable to SoTA non-progressive methods.
  arXiv  Detail & Related papers  (2025-03-11T15:01:11Z)
• MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation [16.68306233403755]
  3D Gaussian Splatting demonstrates excellent quality and speed in novel view synthesis. The huge file size of the 3D Gaussians presents challenges for transmission and storage. MesonGS significantly reduces the size of 3D Gaussians while preserving competitive quality.
  arXiv  Detail & Related papers  (2024-09-15T14:58:20Z)
• Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields [13.729716867839509]
  We propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering.
  arXiv  Detail & Related papers  (2024-08-07T14:56:34Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• 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)
• LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS [55.85673901231235]
  We introduce LightGaussian, a method for transforming 3D Gaussians into a more compact format. Inspired by Network Pruning, LightGaussian identifies Gaussians with minimal global significance on scene reconstruction. LightGaussian achieves an average 15x compression rate while boosting FPS from 144 to 237 within the 3D-GS framework.
  arXiv  Detail & Related papers  (2023-11-28T21:39:20Z)

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.