ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction

• URL: http://arxiv.org/abs/2505.08196v1
• Date: Tue, 13 May 2025 03:13:40 GMT
• Title: ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction
• Authors: He Huang, Qi Yang, Mufan Liu, Yiling Xu, Zhu Li,
• Abstract summary: Existing 4D Gaussian Splatting methods rely on per-Gaussian deformation from a canonical space to target frames.<n>We propose Anchor-Driven Deformable and Compressed Gaussian Splatting (ADC-GS), a compact and efficient representation for dynamic scene reconstruction.<n>We show that ADC-GS outperforms the per-Gaussian deformation approaches in rendering speed by 300%-800%.
• Score: 22.24139713363786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing 4D Gaussian Splatting methods rely on per-Gaussian deformation from a canonical space to target frames, which overlooks redundancy among adjacent Gaussian primitives and results in suboptimal performance. To address this limitation, we propose Anchor-Driven Deformable and Compressed Gaussian Splatting (ADC-GS), a compact and efficient representation for dynamic scene reconstruction. Specifically, ADC-GS organizes Gaussian primitives into an anchor-based structure within the canonical space, enhanced by a temporal significance-based anchor refinement strategy. To reduce deformation redundancy, ADC-GS introduces a hierarchical coarse-to-fine pipeline that captures motions at varying granularities. Moreover, a rate-distortion optimization is adopted to achieve an optimal balance between bitrate consumption and representation fidelity. Experimental results demonstrate that ADC-GS outperforms the per-Gaussian deformation approaches in rendering speed by 300%-800% while achieving state-of-the-art storage efficiency without compromising rendering quality. The code is released at https://github.com/H-Huang774/ADC-GS.git.

Related papers

• Structure-Guided Allocation of 2D Gaussians for Image Representation and Compression [26.855464287699366]
  We propose a structure-guided allocation principle for 2DGS, which explicitly couples image structure with both representation capacity and quantization precision.<n>We show that our approach substantially improves both the representational power and the gradient performance of 2DGS while maintaining over 1000 FPS decoding.
  arXiv  Detail & Related papers  (2025-12-30T06:35:46Z)
• Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy-filtering Training on Streaming Content [28.476493987089466]
  This paper presents M, a scalable Gaussian Splatting framework for efficient training in streaming tasks.<n>M achieves superior visual quality while significantly reducing training time compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-29T09:13:04Z)
• Gradient-Direction-Aware Density Control for 3D Gaussian Splatting [7.234328187876289]
  3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis through explicit scene representation.<n>Existing approaches manifest two critical limitations in complex scenarios.<n>We present Gradient-Direction-Aware Gaussian Splatting (GDAGS), a gradient-direction-aware adaptive density control framework.
  arXiv  Detail & Related papers  (2025-08-12T13:12:54Z)
• Steepest Descent Density Control for Compact 3D Gaussian Splatting [72.54055499344052]
  3D Gaussian Splatting (3DGS) has emerged as a powerful real-time, high-resolution novel view.<n>We propose a theoretical framework that demystifies and improves density control in 3DGS.<n>We introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud.
  arXiv  Detail & Related papers  (2025-05-08T18:41:38Z)
• Micro-splatting: Maximizing Isotropic Constraints for Refined Optimization in 3D Gaussian Splatting [0.3749861135832072]
  This work implements an adaptive densification strategy that dynamically refines regions with high image gradients.<n>It results in a denser and more detailed gaussian means where needed, without sacrificing rendering efficiency.
  arXiv  Detail & Related papers  (2025-04-08T07:15:58Z)
• 4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video [56.04182926886754]
  3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences.<n>Existing methods typically handle dynamic 3DGS representation and compression separately, motion information and the rate-distortion trade-off during training.<n>We propose 4DGC, a rate-aware 4D Gaussian compression framework that significantly reduces storage size while maintaining superior RD performance for FVV.
  arXiv  Detail & Related papers  (2025-03-24T08:05:27Z)
• 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)
• Efficient Density Control for 3D Gaussian Splatting [3.6379656024631215]
  3D Gaussian Splatting (3DGS) has demonstrated outstanding performance in novel view synthesis.<n>We propose two key innovations: (1) Long-Axis Split, which precisely controls the position, shape, and opacity of child Gaussians; and (2) Recovery-Aware Pruning, which leverages differences in recovery speed after resetting opacity to prune overfitted Gaussians.
  arXiv  Detail & Related papers  (2024-11-15T12:12:56Z)
• 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.