Title: Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Gaussian splatting

model compaction

Gaussian mixture reduction

composite transport divergence

optimal transport
Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for radiance field rendering, but it typically requires millions of redundant Gaussian primitives, overwhelming memory and rendering budgets. Existing compaction approaches address this by pruning Gaussians based on heuristic importance scores, without global fidelity guarantee. To bridge this gap, we propose a novel optimal transport perspective that casts 3DGS compaction as global Gaussian mixture reduction. Specifically, we first minimize the composite transport divergence over a KD-tree partition to produce a compact geometric representation, and then decouple appearance from geometry by fine-tuning color and opacity attributes with far fewer Gaussian primitives. Experiments on benchmark datasets show that our method (i) yields negligible loss in rendering quality (PSNR, SSIM, LPIPS) compared to vanilla 3DGS with only 10\% Gaussians; and (ii) consistently outperforms state-of-the-art 3DGS compaction techniques. Notably, our method is applicable to any stage of vanilla or accelerated 3DGS pipelines, providing an efficient and agnostic pathway to lightweight neural rendering.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a compaction method for pre-trained 3DGS scenes that first optimizes a Gaussian mixture model to represent the scene with fewer primitives, followed by a color finetuning that re establishes high quality textures. The method can be applied on top of existing state-of-the-art 3DGS variants for even better rendering quality. As a result, the proposed method achieves high rendering quality, measured with PSNR, SSIM and LPIPS, while using only 10% of the primitive count. To reduce the overall computing time and memory consumption, the scene is first split into blocks using a KD-tree and the GMR is performed on each block separately.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper is written very well\n- The idea is clear and the explanations are easy to follow\n- The results are impressive\n- The visualizations, algorithms and formulas support the understanding\n\nWeaknesses:\n- The additional computing time / memory consumption is not reported. This is the biggest flaw, as it gives no intuition if the proposed method takes minutes, hours or days to compute.\n- Comparison to Mini-splatting (set to produce 10% or 20% primitives) is missing. Only Mini-splatting-D is reported\n- Line 289: “as demonstrated in the ‘Bicycle’ scene, where the poor performance of the Atom-GS baseline prevents our approach from maintaining”. This does not align with my observation in Figure 4. The grass is clearly better in the “before“ image.\n- The random subsampling is barely described and possibly not the best baseline\n- The ablation study is very limited. Different cost functions could be compared (for instance: only position, only covariance, additional opacity term)\n- Absolute number of Gaussian primitives should be mentioned in the main paper at least once\n- Algorithm 2 is not well formatted \n\n\nNice to have:\n- Experiments that use LPIPS as a loss during appearance optimization\n- Source code or example .ply files in the supplementary materials\n- Experiments that finetune the geometry as well during appearance optimization\n- large Gaussian primitives that overlap with neighboring blocks are not regarded for GMR\n\nQUESTIONS:\nWhat is the computing time and memory consumpty of the algorithm?\n\nHow does the method compare to Mini-splatting set to a 10% or 20% primitive count?\n\nWhat can be done to improve the LPIPS quality?\n\nHow can you ensure that the settings / loss functions are optimal? Have you made further ablations for different cost functions / fine tuning settings that are not included in the paper?\n\nLIMITATIONS:\nThe limitations are barely discussed. The authors acknowledge a decrease in LPIPS performance but leave out important information about the runtime and memory consumption.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a method that makes 3D Gaussian Splatting representations compact.\n\nThe main pipeline is\n- Start with an optimized 3D representation.\n- Partition the 3D space using a KD-tree.\n- Within each partition, run Gaussian Mixture Reduction (GMR) to decrease the number of Gaussians.\n- Refine the appearance (colors) to obtain the final result.\n\nThe authors demonstrate that this pipeline can be applied on top of several existing methods, including the original 3DGS, Mini-Splatting, and AtomGS.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- Employing GMR is an interesting idea, and combining it with spatial partitioning via a KD-Tree effectively reduces computational cost.\n- The storyline, from the optimal-transport to engineering details such as the KD-Tree, is easy to follow.\n\n\n**Weaknesses**\n\n- The experimental comparison is not thorough. Given the rapid advancements in this field, the paper should highlight its advantages more clearly. If the focus is post-training compactness, it should be compared with additional baselines such as MesonGS (ECCV 2024) and Fast Compression of 3D Gaussian Splatting (ICLR 2025). It would also be helpful to compare against end-to-end compact 3DGS variants such as ContextGS (NeurIPS 2024) and LocoGS (ICLR 2025).\n- Rate–distortion (RD) curves are essential for comparison, yet Fig. 5 shows only the proposed method. Comparing RD curves to those of other methods would be helpful in demonstrating the effectiveness of the proposed method.\n\nQUESTIONS:\n- Would it be possible to include comparisons with a broader set of methods? This would highlight how effective the proposed GMR-based approach is relative to other techniques. Given this intriguing idea, I’d like to raise my initial score once those comparisons are more solid.\n- The limitation section seems to be missing. Could you discuss the limitations during the rebuttal?\n\nLIMITATIONS:\nNeither the main paper nor the supplementary material contains a Limitations section, which conflicts with the required checklist.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 4, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the problem of reducing the number of Gaussians needed to represent a scene, using an optimal transport perspective. The authors first train a 3DGS model. The resulting Gaussians are then partitioned into blocks based on camera centers. In the first stage, the geometric parameters (i.e., centers and covariances) are iteratively adjusted using K-means to minimize the divergence between the original Gaussian mixture and the changed mixture. After this geometric optimization, the appearance parameters (color and opacity) are refined. The method is evaluated on three datasets, and the results show that, even when retaining only 10% of the Gaussians, the final rendering quality is largely preserved.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\nMinimizing the number of Gaussians required to represent a scene is a relevant and timely research problem. The paper presents a novel approach that effectively reduces the number of Gaussians to 10% of the original set, while maintaining only marginal degradation in rendering quality.\n\nWeakness:\n\nThe paper does not provide any information on the computational cost of applying the proposed method. Please include the runtime to help assess the practical overhead of integrating your method.\n\nParts of Figure 3 are difficult to read due to small font sizes. Consider enlarging the fonts or the entire figure for better clarity.\n\nYou cite [24] as another probabilistic approach that, while different in methodology, also performs well with fewer Gaussians compared to 3DGS. Please include this method in the comparisons (in Table 2), as it would be valuable to see how your approach performs compared to that.\n\nFigure 4 needs correction. It contains a typo (\"PNSR\" instead of \"PSNR\") and the PSNR values appear to be mislabeled or inconsistent. Moreover, the visual differences between the “before” and “after” images are not clearly visible, even when zoomed in. It would be helpful to include a difference map to better highlight the changes introduced by your method, as they are not significant.\n\nQUESTIONS:\nAs provided in the weakness section:\n\nPlease include timing results for your approach to help assess its computational cost.\n\nPlease add a comparison to [24] in Table 2, as it is also a relevant probabilistic method that performs well with fewer Gaussians.\n\nPlease revise Figures 3 and 4 for clarity and accuracy:\n– In Figure 3, increase the font size or enlarge the image for better readability.\n– In Figure 4, correct the \"PNSR\" typo (should be \"PSNR\"), verify the PSNR values (they appear to be switched), and consider adding a difference map to make the visual impact of your method more evident.\n\nLIMITATIONS:\n- Please provide the limitations of your technique and possible future directions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Gaussian Herding Across Pens (GHAP), a Gaussian compaction method that integrates similar Gaussians, instead of pruning less-important Gaussians. The compaction pipeline consists of two stages, (a) geometric compaction via Gaussian Mixture Reduction (GMR) and (b) appearance finetuning. To enhance the efficiency, these compaction steps are conducted in a block-wise manner divided by KD-tree partition. Experimental results show that GHAP reduces the number of Gaussians of existing representations as a plug-and-play module. Also, it achieves comparable rendering quality with the same compaction setting compared to existing Gaussian compaction methods such as LightGaussian and PUP-3DGS.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n- While the existing compaction methods reduce the number of Gaussians by pruning, this method integrates similar Gaussians.\n- This method can be adapted to existing Gaussian representations as a plug-and-play module. \n- Experimental results indicate that this method can maintain high fidelity despite a small retention rate.\n\n---\n\n### Weaknesses\n\n- To validate the effectiveness of the KD-tree partitioning strategy, it would be recommended to provide an ablation study on it.\n\n- The technical contribution of this paper is a simple compaction method for 3D Gaussians, which may be insufficient to be accepted at this conference.\n\n- The experimental results show less effective results according to the experimental setting. To be specific, it cannot outperform all existing compaction methods for all datasets. Despite the high fidelity, comparative methods show better scores for some experimental settings, as shown in Table 2.\n\nQUESTIONS:\n- In Table 2, the rendering quality of LightGaussian outperforms the rendering quality of this method for a 10% retention rate on Mip-NeRF 360 dataset. Mip-NeRF 360 dataset contains both indoor and outdoor scenes which have been employed as a standard real-world benchmark for novel-view synthesis. Can you explain why this method shows worse performance on Mip-NeRF 360 dataset?\n\n- For better understanding, adding RD curves for Table 1 and Table 2 would be helpful.\n\n- Although several compaction methods are mentioned in Section 2, the authors provide a comparison only with LightGaussian and PUP-3DGS. It is recommended to conduct an additional comparison with RadSplat, Mini-Splatting, and Trimming-the-Fat.\n\nLIMITATIONS:\n- Despite the effectiveness of the proposed compaction strategy, this paper contains only limited technical contributions to be accepted at this conference. \n\n- Although the experimental results show comparable performance, they are insufficient to clearly demonstrate that the proposed method outperforms existing approaches.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}