Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis

Yuqin Lu; Yihua Dai; Yang Zhou; Guiqing Li; Shengfeng He

cs.CVApr 13, 2026

Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis

Authors: Yuqin Lu, Yihua Dai, Yang Zhou, Guiqing Li, Shengfeng He

Organizations: School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China · School of Computing and Information Systems, Singapore Management University, Singapore 188065

Abstract

3D Gaussian Splatting (3DGS) has become a state-of-the-art framework for real-time, high-fidelity novel view synthesis. However, its substantial storage requirements and inherently unstructured representation pose challenges for deployment in streaming and resource-constrained environments. Existing Level-of-Detail (LOD) strategies, particularly those based on bottom-up construction, often introduce redundancy or lead to fidelity degradation. To overcome these limitations, we propose Iterative Gaussian Synopsis, a novel framework for compact and progressive rendering through a top-down "unfolding" scheme. Our approach begins with a full-resolution 3DGS model and iteratively derives coarser LODs using an adaptive, learnable mask-based pruning mechanism. This process constructs a multi-level hierarchy that preserves visual quality while improving efficiency. We integrate hierarchical spatial grids, which capture the global scene structure, with a shared Anchor Codebook that models localized details. This combination produces a compact yet expressive feature representation, designed to minimize redundancy and support efficient, level-specific adaptation. The unfolding mechanism promotes inter-layer reusability and requires only minimal data overhead for progressive refinement. Experiments show that our method maintains high rendering quality across all LODs while achieving substantial storage reduction. These results demonstrate the practicality and scalability of our approach for real-time 3DGS rendering in bandwidth- and memory-constrained scenarios.

Figures & tables

Fig. 1: Iterative Gaussian Synopsis enables hierarchical “unfolding” of 3D Gaussian Splatting. The top rows illustrate the anchor structure alongside the derived 3D Gaussians at both full resolution and progressively condensed levels. The bottom rows compare the Level-of-Detail progression from a compact synopsis (starting at 6.5MB) to the highest fidelity (50.7MB) for our method, against a standard 3DGS baseline with a total size of 601.1MB. Our approach achieves clearer intermediate representations and significantly improved efficiency, delivering comparable visual quality at a fraction of the storage cost.

Fig. 2: Comparison of LOD Construction Strategies. Left (Bottom-Up Increment): A conventional bottom-up approach that builds from a coarse base layer by sequentially adding enhancement layers, typically requiring updates to the opacity or parameters of previous layers. Right (Top-Down Unfolding): Our proposed top-down unfolding strategy, where lower levels of detail are systematically derived from a full-fidelity model, ensuring coherent simplification and improved inter-layer consistency.

Fig. 3: Overview of the Iterative Gaussian Synopsis Framework. Top: The full-resolution 3DGS model is progressively “unfolded” into coarser LODs using a learnable, mask-guided pruning strategy. Bottom: The resulting hierarchical LODs support efficient progressive rendering, where visual quality improves smoothly as more data is streamed, transitioning seamlessly from a coarse structural outline to a high-fidelity reconstruction.

Fig. 4: Our hierarchical architecture. The hierarchical grids capture multi-scale contextual information while the learnable codebook models localized details, formimg the foundation for unfolding into LODs.

Level	Metric	Mip-NeRF360 [ 43 ]				BungeeNeRF [ 40 ]				DeepBlending [ 45 ]				Tanks&Temples [ 44 ]
Level	Metric	3DGS	Oct-GS	LapisGS	Ours	3DGS	Oct-GS	LapisGS	Ours	3DGS	Oct-GS	LapisGS	Ours	3DGS	Oct-GS	LapisGS	Ours
L0	PSNR $\uparrow$	18.39	17.44	28.10	28.02	17.05	19.20	26.23	27.23	20.55	22.56	19.99	27.95	15.10	12.55	23.41	23.68
	SSIM $\uparrow$	0.465	0.447	0.869	0.860	0.473	0.594	0.886	0.912	0.614	0.720	0.569	0.861	0.492	0.342	0.864	0.865
	LPIPS $\downarrow$	0.380	0.446	0.117	0.134	0.384	0.293	0.108	0.106	0.324	0.282	0.374	0.142	0.347	0.512	0.119	0.123
	FPS $\uparrow$	189	180	151	183	157	132	158	159	191	182	125	188	202	229	178	185
	Size $\downarrow$	99.41	12.40	304.24	9.79	204.59	60.76	253.82	27.36	87.97	12.80	554.02	5.95	52.74	5.44	181.72	6.09

TABLE I: Quantitative comparisons of our method against 3DGS [ 1 ] , Octree-GS (Oct-GS) [ 15 ] , and LapisGS [ 18 ] across different datasets and LOD levels. Best and second-best results are highlighted in red and yellow , respectively. Size is measured in MB. It should be noted that the reported sizes for 3DGS and LapisGS represent their default, uncompressed explicit Gaussian representations. Following LapisGS, metrics at each LOD are computed at the proportionally scaled target resolution of that level for every method. Coarser levels therefore face easier targets and are comparable only within a level.

Fig. 5: Qualitative comparison of LOD rendering across different methods. Our method yields superior visual quality (higher PSNR, and clearer details as shown in zoomed-in views) at each level, while requiring substantially less storage compared to all baseline methods.

Panel of Figure 7 — Fig. 6: Quantitative results of the ablation study for our adaptation modules. Across all metrics (PSNR, SSIM, LPIPS) and at every level, the full integration of both modules yields the highest fidelity, validating the effectiveness of our framework.

	Level	Scaffold-GS (Independent)				Ours (Unified Progressive)
	Level	PSNR	SSIM	Tr.(min)	Cum. Size	PSNR	SSIM	Tr.(min)	Cum. Size
Mip.	L0	28.75	0.874	12.2	89.80 MB	28.02	0.860	4.5	9.79 MB
	L1	28.15	0.850	17.3	223.23 MB	27.64	0.842	9.0	27.65 MB
	L2	27.23	0.813	23.1	388.47 MB	26.95	0.796	12.4	62.79 MB
	L3	27.09	0.793	39.0	560.27 MB	27.17	0.790	32.2	90.11 MB
DB	L0	29.13	0.885	7.4	39.10 MB	27.95	0.861	5.0	5.95 MB

TABLE II: Architectural Analysis across multiple datasets. Our unified, progressive framework is compared against Scaffold-GS models trained independently at corresponding scaled resolutions.

3D Gaussian Splatting

3D Gaussian Splatting represents scenes as optimizable 3D Gaussian primitives for fast, photorealistic rendering from new viewpoints.

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3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis. However, this technique requires dense training views to accurately reconstruct 3D geometry. A limited number of input views will significantly degrade reconstruction quality, resulting in artifacts such as "floaters" and "background collapse" at unseen viewpoints. In this work, we introduce SparseGS, an efficient training pipeline designed to address the limitations of 3DGS in scenarios with sparse training views. SparseGS incorporates depth priors, novel depth rendering techniques, and a pruning heuristic to mitigate floater artifacts, alongside an Unseen Viewpoint Regularization module to alleviate background collapses. Our extensive evaluations on the Mip-NeRF360, LLFF, and DTU datasets demonstrate that SparseGS achieves high-quality reconstruction in both unbounded and forward-facing scenarios, with as few as 12 and 3 input images, respectively, while maintaining fast training and real-time rendering capabilities.

Abstract

3D Gaussian Splatting

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