ClunoGS: Compact Language 3D Gaussian Splatting via Unified Optimization

Jian Xu; Yi Li; Ying Huang; Siyi Wu; Bingzhe Li; Yuede Ji; Miao Yin

cs.CVOct 21, 2026

ClunoGS: Compact Language 3D Gaussian Splatting via Unified Optimization

Authors: Jian Xu, Yi Li, Ying Huang, Siyi Wu, Bingzhe Li, Yuede Ji, Miao Yin

Abstract

Language 3D Gaussian Splatting enables open-vocabulary scene understanding by associating Gaussian primitives with language features. These semantic attributes increase model storage, while most existing 3DGS compression methods are designed mainly to preserve RGB rendering quality. One straightforward approach is to apply sparsification and quantization sequentially. However, pruning based only on rendering quality can remove Gaussians that have small RGB contributions but are important for semantic prediction. Once removed, their semantic information cannot be recovered by later quantization. We propose ClunoGS, a compact framework for Language 3DGS that combines semantics-aware Gaussian sparsification and vector quantization in a unified constrained objective. Semantic supervision is used during sparsification, and the objective jointly considers rendering quality, semantic accuracy, and compactness. We optimize the unified objective with alternating primal, sparsification, quantization, and dual updates. We evaluate ClunoGS on open-vocabulary localization and segmentation using the LERF and ScanNet-10 benchmarks. Under aggressive compression, our unified, semantics-aware formulation substantially alleviates the degradation of semantic representations caused by sequential compression and semantics-agnostic Gaussian sparsification. Ablations further show that jointly coordinating sparsification and quantization better preserves semantic accuracy than applying them independently.

Figures & tables

Figure 2: Overview of the ClunoGS framework, illustrating the four updates performed in each iteration of the optimization loop. The diagram suppresses the language-loss weight and attribute-group indices.

Method	Quality $\uparrow$			Efficiency $\downarrow$
	mIoU	Acc.	PSNR	Storage (MB)	Peak Mem. (GB)
Occam’s LGS	56.8	82.5	24.08	7663.23	17.83
Dr. Splat	43.3	73.3	24.22	467.85	6.72
LangSplatV2	59.9	84.1	24.22	345.94	5.54
CCL-LGS	57.8	81.0	24.11	735.93	5.21
GAGS	53.9	75.7	24.22	399.06	8.96

Table 1: Overall performance. (a) LERF four-scene macro averages, where Acc. denotes localization accuracy. (b) ScanNet-10 ten-scene macro averages, where Acc. denotes mean class accuracy. Ay First , Ay Second , and Ay Third .

Figure 3: PCA visualizations of rendered semantic maps. Under the same 20% Gaussian-retention budget, sequential compression blurs semantic boundaries and can remove semantic regions, whereas our joint method better preserves their structure.

Sparsifier	Quantizer	#G	mIoU $\uparrow$	Acc. $\uparrow$	PSNR $\uparrow$	Storage $\downarrow$
None	None	1.27M	59.9	84.1	24.22	345.94
Opacity Top- $K$	CompGS	0.25M	51.4	71.9	22.12	59.35
Opacity Top- $K$	C3DGS	0.25M	53.2	77.5	22.01	64.26
PUP 3D-GS	CompGS	0.25M	53.1	69.1	23.84	59.35
PUP 3D-GS	C3DGS	0.25M	54.4	73.5	23.97	64.26
GaussianSpa	CompGS	0.25M	49.0	74.0	24.01	59.35

Table 2: Sequential sparsification and quantization on LERF. All compressed rows use the same 20% Gaussian-retention budget; storage is in MB. Colors indicate quality rankings and exclude the uncompressed reference.

Method	Pipeline	mIoU $\uparrow$	Acc. $\uparrow$	PSNR $\uparrow$	#G $\downarrow$	Storage (MB) $\downarrow$
LangSplatV2	Original	59.9	84.1	24.22	1.27M	345.94
	Sem. $\rightarrow$ Compress	49.0	74.0	24.01	0.25M	59.35
	Compress $\rightarrow$ Sem.	52.4	77.9	23.80	0.25M	59.35
Dr. Splat	Original	43.3	73.3	24.22	1.27M	467.85
	Sem. $\rightarrow$ Compress	27.1	59.3	24.00	0.25M	101.69
	Compress $\rightarrow$ Sem.	29.1	60.8	24.00	0.25M	101.69

Table 3: Ordering semantic learning and compression.

Variant	mIoU $\uparrow$	Acc. $\uparrow$	PSNR $\uparrow$	Storage (MB) $\downarrow$	#G $\downarrow$
Uncompressed model	59.9	84.1	24.22	345.94	1.27M
Sparsification only	58.5	83.3	24.20	173.07	0.25M
VQ only	59.0	84.0	24.19	118.46	1.27M
Full	58.3	83.0	24.15	59.35	0.25M

Table 4: Component ablations on LERF. The rows are not rate-matched; #G and storage report each operating point.

Group	Variant	mIoU $\uparrow$	Acc. $\uparrow$	PSNR $\uparrow$
Design components	w/o semantic guidance	49.0	74.0	24.01
Design components	Penalty only	44.6	68.1	22.81
Optimization strategy	Sparsify $\rightarrow$ VQ	48.3	70.0	22.35
	VQ $\rightarrow$ Sparsify	47.2	68.9	23.24
	Joint (ours)	58.3	83.0	24.15

Table 5: Optimization ablations on LERF. All variants use the same initialization, views, and budget, with 0.25M Gaussians and 59.35 MB of storage.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Setting	Value
3DGS initialization	30k iterations
Semantic field	3 levels
Standard language-field stage	First 20k iterations
Unified optimization stage	Final 10k iterations
Gaussian-retention budget	20%
SH codebook	256 entries

Appendix

Table S1 : Main-model implementation settings.

3DGS Compression

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

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Sparse Code Uplifting for Efficient 3D Language Gaussian Splatting

Lovre Antonio Budimir, Yushi Guan, Steve Ryhner, Sven Lončarić+1

Faculty of Electrical Engineering and Computing, University of Zagreb · Department of Computer Science, University of Toronto · Vector Institute

3D Gaussian Splatting Language-Embedded 3DGS Representation Learning 3DGS Acceleration Sparse Coding

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3D Language Gaussian Splatting (3DLGS) augments 3D Gaussian Splatting with language-aligned visual features for open-vocabulary 3D scene understanding. A core challenge is efficiently associating high-dimensional vision-language embeddings with millions of 3D Gaussians while preserving efficient feature rendering for text-based querying. Existing methods either store dense features directly on Gaussians, causing high storage costs and slow rendering, or learn compact representations through expensive per-scene optimization with repeated feature rasterization. No existing method simultaneously achieves fast 3D semantic reconstruction, efficient storage, and fast rendering. We propose SCOUP (Sparse COde UPlifting), which addresses all three by decoupling language representation learning from 3D Gaussian optimization. Rather than working directly in 3D, we learn sparse codebook-based representations entirely using features associated with 2D image regions, associating each region with a sparse set of codebook coefficients. We then uplift these coefficients to 3D Gaussians with our weighted sparse aggregation using Gaussian-to-pixel associations, where each Gaussian accumulates coefficients over codebook atoms across views. Top-

K

filtering then extracts the most dominant multi-view coefficients per Gaussian, enabling efficient storage and fast rendering. Our method achieves up to

400\times

training speedup while being

3\times

more memory efficient during training compared to the state-of-the-art in rendering speed. Across multiple benchmarks, SCOUP matches or outperforms existing methods in open-vocabulary querying accuracy.

Abstract

3DGS Compression

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