TinySplat: Feedforward Approach for Generating Compact 3D Scene Representation

• URL: http://arxiv.org/abs/2506.09479v1
• Date: Wed, 11 Jun 2025 07:47:19 GMT
• Title: TinySplat: Feedforward Approach for Generating Compact 3D Scene Representation
• Authors: Zetian Song, Jiaye Fu, Jiaqi Zhang, Xiaohan Lu, Chuanmin Jia, Siwei Ma, Wen Gao,
• Abstract summary: TinySplat is a complete feedforward approach for generating compact 3D scene representations.<n>Built upon standard feedforward 3DGS methods, TinySplat integrates a training-free compression framework.<n>Our framework requires only 25% of the encoding time and 1% of the decoding time.
• Score: 38.50388562890992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent development of feedforward 3D Gaussian Splatting (3DGS) presents a new paradigm to reconstruct 3D scenes. Using neural networks trained on large-scale multi-view datasets, it can directly infer 3DGS representations from sparse input views. Although the feedforward approach achieves high reconstruction speed, it still suffers from the substantial storage cost of 3D Gaussians. Existing 3DGS compression methods relying on scene-wise optimization are not applicable due to architectural incompatibilities. To overcome this limitation, we propose TinySplat, a complete feedforward approach for generating compact 3D scene representations. Built upon standard feedforward 3DGS methods, TinySplat integrates a training-free compression framework that systematically eliminates key sources of redundancy. Specifically, we introduce View-Projection Transformation (VPT) to reduce geometric redundancy by projecting geometric parameters into a more compact space. We further present Visibility-Aware Basis Reduction (VABR), which mitigates perceptual redundancy by aligning feature energy along dominant viewing directions via basis transformation. Lastly, spatial redundancy is addressed through an off-the-shelf video codec. Comprehensive experimental results on multiple benchmark datasets demonstrate that TinySplat achieves over 100x compression for 3D Gaussian data generated by feedforward methods. Compared to the state-of-the-art compression approach, we achieve comparable quality with only 6% of the storage size. Meanwhile, our compression framework requires only 25% of the encoding time and 1% of the decoding time.

Related papers

• CompGS++: Compressed Gaussian Splatting for Static and Dynamic Scene Representation [60.712165339762116]
  CompGS++ is a novel framework that leverages compact Gaussian primitives to achieve accurate 3D modeling.<n>Our design is based on the principle of eliminating redundancy both between and within primitives.<n>Our implementation will be made publicly available on GitHub to facilitate further research.
  arXiv  Detail & Related papers  (2025-04-17T15:33:01Z)
• 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.<n>Current 3D GS compression research primarily focuses on developing more compact scene representations.<n>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.<n>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)
• 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)
• 3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods [10.122120872952296]
  3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering.<n>Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands.<n>This survey provides a detailed examination of compression and compaction techniques developed to make 3DGS more efficient.
  arXiv  Detail & Related papers  (2024-06-17T11:43:38Z)
• 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)
• 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)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)

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.