Compressing 3D Gaussian Splatting via Cross-Representation Priors

Yezheng Zhang; Huanxiong Liang; Chuqin Zhou; Guo Lu; Wenjun Zhang

cs.CVSep 19, 2026

Compressing 3D Gaussian Splatting via Cross-Representation Priors

Authors: Yezheng Zhang, Huanxiong Liang, Chuqin Zhou, Guo Lu, Wenjun Zhang

Organizations: Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract

3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis but incurs high storage and transmission costs due to dense Gaussian primitives. Recent anchor-based compression reduces per-primitive redundancy, yet redundancy across anchors remains largely unexploited. We propose CRP-GS (Cross-Representation Priors for Gaussian Splatting), a rate-distortion optimized compression framework that leverages cross-representation priors to improve anchor-level entropy modeling. First, a Correspondence-Oriented Hierarchical Structure (COHS) organizes anchors by feature correspondence rather than spatial proximity, constructing root-leaf dependencies so that selected anchors can act as informative priors to conditionally encode others, yielding more accurate likelihood prediction and lower conditional entropy. Second, Shared Feature Aggregation (SFA) extracts globally shared features from a contextual hash grid and injects them into anchor representations, factoring out scene-consistent low-frequency information that would otherwise be redundantly embedded in individual anchors. Both modules are trained under a unified rate-distortion objective to balance bitrate reduction and rendering fidelity. Experiments across multiple benchmarks show that CRP-GS achieves a favorable overall rate-distortion trade-off, yielding around 30% average bitrate reduction compared to anchor-based baselines while maintaining comparable rendering quality.

Figures & tables

Fig. 2: CRP-GS employs cross-representation priors to jointly optimize rendering quality and bitrate. The rate path organizes anchors into a Correspondence-Oriented Hierarchical Structure (COHS) for conditional entropy coding, while the distortion path constructs enriched anchors via Shared Feature Aggregation (SFA) for high-fidelity rendering. Both paths are optimized under a unified rate-distortion training scheme.

Fig. 3: CDF plot of the number of correspondence anchors under different distances and thresholds. Distances are normalized by the scene diagonal length, defined as the diagonal of the anchor-set axis-aligned bounding box. The curves are computed from 10,000 uniformly sampled query anchors from the Bicycle scene of Mip-NeRF360 [ 57 ] , and the legend values denote cosine-similarity thresholds.

Fig. 4: Effect of SFA. Rendering with only the individual anchor feature ( $f_{i}$ , left) exhibits missing structures and local inconsistencies. The shared feature queried from the hash grid ( $f_{s}$ , middle-left) captures coarse but globally consistent scene information. Aggregating $f_{s}$ with $f_{i}$ yields the aggregated feature ( $f_{a}$ , middle-right), producing a more coherent reconstruction that better matches the ground truth (right).

Methods	Mip-NeRF360				Tanks&Temples				DeepBlending				BungeeNeRF
Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	SIZE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	SIZE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	SIZE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	SIZE $\downarrow$
3DGS	27.49	0.813	0.222	744.7	23.69	0.844	0.178	431.0	29.42	0.899	0.247	663.9	24.87	0.841	0.205	1616
Scaffold-GS	27.50	0.806	0.252	253.9	23.96	0.853	0.177	86.50	30.21	0.906	0.254	66.00	26.62	0.865	0.241	183.0
EAGLES	27.15	0.808	0.238	68.89	23.41	0.840	0.200	34.00	29.72	0.906	0.249	52.34	25.89	0.865	0.197	115.2
LightGaussian	27.00	0.799	0.249	44.54	22.83	0.822	0.220	22.43	27.01	0.872	0.308	33.94	24.52	0.825	0.255	87.28
Compressed3D	26.98	0.801	0.238	28.80	23.32	0.832	0.194	17.28	29.38	0.898	0.253	25.30	24.13	0.802	0.245	55.79

TABLE I: 3DGS Compression Benchmark Comparison

Datasets	vs. HAC		vs. ContextGS		vs. HAC++		vs. HEMGS		vs. PCGS
Datasets	BD-rate (%) $\downarrow$	BD-PSNR (dB) $\uparrow$	BD-rate (%) $\downarrow$	BD-PSNR (dB) $\uparrow$	BD-rate (%) $\downarrow$	BD-PSNR (dB) $\uparrow$	BD-rate (%) $\downarrow$	BD-PSNR (dB) $\uparrow$	BD-rate (%) $\downarrow$	BD-PSNR (dB) $\uparrow$
Mip-NeRF360	-39.28	+0.334	–	+0.204	-1.97	+0.047	-8.99	+0.050	-14.32	+0.083
Tanks&Temples	-56.90	+0.586	-52.41	+0.420	-24.31	+0.208	-6.82	+0.035	-36.02	+0.277
DeepBlending	-44.25	+0.591	-30.78	+0.208	-2.30	+0.045	-8.02	+0.054	-23.76	+0.171
BungeeNeRF	-27.06	+0.595	+15.24	-0.147	+32.56	-0.282	–	–	+41.96	-0.287
Average	-41.87	+0.526	-22.65	+0.171	+0.99	+0.005	-7.94	+0.046	-8.04	+0.061

TABLE II: BD-rate and BD-PSNR comparison over the overlapping rate-distortion range.

Fig. 5: The rate-distortion curves comparing our CRP-GS with baseline methods. Note that file size directly reflects the entropy-coded bitstream and is proportional to the effective bitrate under identical rendering settings.

Fig. 6: Qualitative comparisons of “counter” from Mip-NeRF360 [ 57 ] and “truck” from Tanks&Temples [ 60 ] .

Method	Train [ 60 ]		Room [ 57 ]		DrJohnson [ 59 ]
Method	PSNR $\uparrow$	Model Size $\downarrow$	PSNR $\uparrow$	Model Size $\downarrow$	PSNR $\uparrow$	Model Size $\downarrow$
Our full model	22.87	4.67	32.01	3.43	29.72	3.71
Our w/o SFA	22.84 (-0.03 dB)	5.73 (+22.7%)	31.91 (-0.10 dB)	3.80 (+11.0%)	29.70 (-0.02 dB)	3.91 (+5.4%)
Our w/o COHS and SFA	22.55 (-0.32 dB)	5.74 (+22.9%)	31.64 (-0.37 dB)	3.94 (+15.0%)	29.60 (-0.11 dB)	3.93 (+6.0%)
Our w/o COHS, SFA, and AC	22.55 (-0.32 dB)	6.59 (+41.1%)	31.64 (-0.37 dB)	4.70 (+37.2%)	29.60 (-0.11 dB)	4.51 (+21.7%)

TABLE III: Ablation study on representative scenes from different datasets.

Method	PSNR (dB)	Model Size (MB)
Full Method (Ours)	22.41	5.89
Random Link	22.35	6.41
Distance Link	22.28	6.22
No Link	22.30	6.13

TABLE IV: Comparison of COHS with alternative linking strategies

Panel of Figure 10 — Fig. 7: (a) Ablation study of the SFA mechanism; (b) Compression heatmap analysis with and without SFA.

Fig. 8: Bitstream breakdown comparison between CRP-GS and HAC.

Item	Size (MB)	Ratio (%)
Active-anchor Mask	0.03	0.60
Link Vector $\mathbf{L}$	0.16	3.14
Total COHS Side Information	0.19	3.74

TABLE V: COHS side information overhead in the final bitstream

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3D Gaussian Splatting compression reduces storage and rendering costs while preserving the quality of reconstructed views and scenes.

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3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spatial context modeling over irregular 3D representations, increasing the complexity of training and coding. Meanwhile, floating-point context inference can introduce numerical inconsistencies across platforms, causing entropy-decoding failures. To address these practical challenges, we propose COSA-GS, which constructs context without spatial aggregation through anchor-wise causal factorization. Specifically, we use geometry context derived from each anchor's coordinates to model a compact learnable anchor latent. The anchor latent is then fused with the geometry context to form an anchor context for attribute coding. The resulting context model features a simple architecture composed solely of linear transformations and activations. We train COSA-GS using rate--distortion optimization with adaptive Gaussian pruning. Further, we develop quantization-aware training and integer inference for the context model to achieve bit-exact consistency of entropy-decoded symbols across platforms. Experiments demonstrate that COSA-GS achieves state-of-the-art compression performance while retaining fast and consistent cross-platform decoding, providing a simple yet effective framework for practical 3DGS compression. Code is available at https://github.com/pengpeng-yu/COSA-GS.

Abstract

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