Title: Reframing Gaussian Splatting Densification with Complexity-Density Consistency of Primitives
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Gaussian Splatting

Novel View Synthesis

3D Reconstruction

Computer Vision
Abstract: The essence of 3D Gaussian Splatting (3DGS) training is to smartly allocate Gaussian primitives, expressing complex regions with more primitives and vice versa. 
Prior researches typically mark out under-reconstructed regions in a rendering-loss-driven manner. 
However, such a loss-driven strategy is often dominated by low-frequency regions, which leads to insufficient modeling of high-frequency details in texture-rich regions. As a result, it yields a suboptimal spatial allocation of Gaussian primitives.
This inspires us to excavate the loss-agnostic visual prior in training views to identify complex regions that need more primitives to model. 
Based on this insight, we propose Complexity-Density Consistent Gaussian Splatting (CDC-GS), which allocates primitives based on the consistency between visual complexity of training views and the density of primitives. 
Specifically, primitives involved in rendering high visual complexity areas are categorized as modeling high complexity regions, where we leverage the high frequency wavelet components of training views to measure the visual complexity. 
And the density of a primitive is computed with the inverse of geometric mean of its distance to the neighboring primitives. 
Guided by the positive correlation between primitive complexity and density, we determine primitives to be densified as well as pruned. 
Extensive experiments demonstrate that our CDC-GS surpasses the baseline methods in rendering quality by a large margin using the same amount of Gaussians. 
And we provide insightful analysis to reveal that our method serves perpendicularly to rendering loss in guiding Gaussian primitive allocation.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a novel method for densifying and pruning Gaussian primitives with respect to the complexity of the training views and the density of the scene. The complexity is modeled with a high pass filter (2D DWT) and assigned to each Gaussian primitive via alpha compositing and the density is modeled with the average distance of each Gaussian to its three closest neighbors. Using both criteria for determining the densification strategy shows to improve the rendering quality without creating too many Gaussian primitives. The authors provide limited ablations and compare their results quantitatively to some of the current 3DGS methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The topic is relevant\n- The idea is interesting\n- The results are promising\n- The evaluation with different high pass filters underlines the choice for the Discrete Wavelet Transform (should be moved to the main paper)\n- Figure 4a demonstrates well the effect of the methods\n\nWarknesses:\n\nIn general, some parts are not clearly explained or confusing:\n- Line 183: “Γmean, Ψmean are the mean of consistency and density” (should be complexity?)\n- Line 195: “the sampling probability is determined with oi” what is oi and what is the intuition?\n- Line 192: how is pd determined from |si| and what is the intuition? High values for |si| indicate good consistency. Why should those regions be further modified?\n- Line 192: It is not clear how under-reconstructed regions and over-reconstructed regions are determined.\n- Why is it required to still use the positional gradient?\n- τlow and τup are set very similarly. It would be interesting if the quality already improves using a fixed threshold of τlow or τup.\n- Where are all hyperparameters coming from? An ablation study would help.\n- Line 230: What is ps?\n- Why is Figure 4b looking completely different to the same graph shown in the appendix Figure 1? \n- Formula 6: Why is the distance to the neighbors calculated with a product and not a sum?\n- The advantages / reasons why the Discrete Wavelet Transform works best, are not discussed\n- The pruning operation is changed without evaluation\n- The runtime is likely increased due to the KNN calculations, but not mentioned in the paper\n- The resolution of the rendered images in Figure 3 is too low\n- The ablation study on T&T is not very expressive as it only includes two scenes\n- More comparisons to related compaction methods from  https://arxiv.org/abs/2407.09510 should be included\n- It would be interesting how well the method integrates into current SOTA methods\n\nQUESTIONS:\nWhile the results show that the proposed method improves the rendering quality, the paper lacks in clarity and evaluation. Although the densification process is the main component of this work, it is not clear how primitives in under-/over-reconstructed regions are selected and how pd is determined. Further, the paper misses intuitive explanations, for instance: how formula 8 is designed, why the positional gradient is still required, or why the DWT is the best choice (other than it works best). Lastly, the paper introduces a lot of parameters while also changing some other components (such as the pruning interval), without proper evaluation / ablation. This makes it difficult to understand what component and which parameters have a big influence on the result. \n\nWithout further evaluation, comparisons and more clear explanations, I cannot confidently accept this paper as a significant contribution to the 3DGS research field.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel method for allocating primitives in 3D Gaussian Splatting. The authors introduce measures of complexity and density for Gaussian primitives to identify under-reconstructed and over-reconstructed regions, and develop an allocation strategy based on these metrics. Experimental results show that the proposed method outperforms baseline approaches overall.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n- S1. The writing is clear and effectively presents main idea. The methodology is straightforward, and the experimental details are well-presented.\n\n- S2. Most of the quantitative results outperform baselines.\n\n### Weaknesses:\n- W1: Figure 4 shows that the proposed method achieves a higher correlation between complexity and density compared to baseline methods. However, since the allocation strategy is explicitly designed based on these two metrics, it is expected that it would outperform baselines on this specific correlation measure. This raises concerns about the fairness of using this metric to claim the method in the paper is better. Moreover, in Figure 4b, the authors illustrate how the correlation score evolves during training. If a higher correlation is considered indicative of better scene modeling or rendering quality, then 3DGS should perform better than other baseline methods. However, the quantitative results do not consistently reflect this. This discrepancy suggests that the correlation metric may not directly align with final rendering performance, and the paper does not sufficiently discuss or justify this gap.\n\nQUESTIONS:\nMy main concern is that the paper does not fully discuss how uneven complexity-density distributions affect rendering performance. I do not doubt the relevance of these two metrics to the 3DGS model, but I believe a deeper discussion is necessary. If the authors could provide a more thorough justification or analysis of this point, I would consider raising my score.\n\nLIMITATIONS:\nYes, the author answered the questions in checklist well and the limitations are fully addressed.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel densification strategy that accounts for both the complexity and density of Gaussian primitives. To achieve this, the authors define the complexity of Gaussians using the discrete wavelet transform (DWT) and the density of Gaussians based on the distance between neighboring Gaussians. Leveraging these two concepts, they introduce a Complexity-Density Consistency (CDC) score to determine which Gaussians deserve densified and a Complexity-Aware Adaptive Threshold (CAAT) to adaptively prune Gaussians based on both gradient and complexity. Experimental results show that the proposed method outperforms the rendering quality of existing Gaussian densification approaches.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- The authors address the limitation of the prior loss-based densification criterion and introduce two important factors for densification, complexity and density of Gaussians.\n- It achieves superior rendering quality compared to existing Gaussian densification approaches, such as Mini-Splatting, Taiming-3DGS, and AbsGS.\n- Detailed analysis of the experimental results demonstrates the significance of complexity and density for effective densification.\n\n---\n\n### Weaknesses\n- CAAT depends on a hyper-parameter \\lambda which requires manual setting for various scene types.\n- Additional computation costs are required for computing the complexity (DWT) and density (kNN) of Gaussian primitives. In particular, kNN search for millions of Gaussians requires a lot of computation overhead.\n- Despite the effectiveness of the proposed method, contributing only a densification criterion without additional technical contributions may be seen as lacking sufficient advancement to be accepted in this conference.\n\nQUESTIONS:\n- In the definition of complexity, why do the authors consider the maximum complexity value for all pixels in training views, instead of an average of them?\n\n- It would be valuable to investigate whether the proposed densification strategy remains effective when targeting a smaller number of Gaussians. To this end, I recommend conducting additional experiments with a more aggressive pruning configuration aimed at reducing the number of Gaussians. These results are recommended to be compared with existing compact 3DGS baselines, such as LightGaussian [NeurIPS'24], Mini-Splatting (not Mini-Splatting-D) [ECCV'24], and Taiming-3DGS [SIGGRAPH Asia'24].\n\n- As I mentioned above, additional costs for computing complexity and density might be required. Please report the comparison for the training time of each method.\n\n- It would be better to add 3DGS-MCMC [NeurIPS'24] in comparison.\n\nLIMITATIONS:\n- Additional computational costs are required for computing the densification standard.\n- Also, limited technical contribution might be seen as a lack of novelty.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Complexity-Density Consistent Gaussian Splatting (CDC-GS), an approach to improve 3D Gaussian Splatting by aligning the allocation of Gaussian primitives with visual complexity rather than relying solely on rendering loss. Traditional loss-driven methods tend to favor low-frequency regions, neglecting high-frequency details. CDC-GS addresses this by leveraging wavelet-based visual complexity from training views and computing primitive density based on spatial proximity. By correlating complexity with density, the method effectively identifies regions for densification and pruning. Experiments show that CDC-GS significantly outperforms baseline methods in rendering quality using the same number of primitives.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper is well-motivated and offers an appropriate analysis of the weaknesses in current practices, which strengthens the rationale behind the proposed solution.\nThe paper introduces a loss-agnostic approach to primitive allocation, shifting away from the conventional rendering-loss-driven strategies. This potentially helps addressing the limitation in existing methods that often overlook high-frequency regions. The proposed complexity-density consistency criterion provides a coherent mechanism for determining where to densify or prune primitives, potentially leading to efficient and accurate scene representations. By leveraging high-frequency components from wavelet transforms to measure visual complexity, the method somehow brings an intuitive and effective signal for guiding primitive allocation, especially in texture-rich areas. The approach complements rendering-loss-based methods by providing an orthogonal signal for guiding Gaussian allocation, making it potentially integrable with existing pipelines.\n\nWeaknesses:\n1. The justification for whether the definitions of complexity and density of Gaussian primitives are appropriate needs to be strengthened.\n2. The paper lacks comparisons with recent state-of-the-art Gaussian Splatting methods, such as Spec-Gaussian, Scaffold-GS, etc.\n3. The improvements appear to be modest rather than significant.\n\nQUESTIONS:\nI would like to see the performance comparison with the SOTA methods, such as Spec-Gaussian etc.\nThe computational overhead may be discussed.\n\nLIMITATIONS:\nThe definitions of visual complexity and primitive density are heuristic and lack strong justification. The computational overhead of complexity and density calculations is not analysed.\nThe method is not compared against recent state-of-the-art approaches like Spec-Gaussian or Scaffold-GS. Reported performance gains are modest.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-04"}