TL;DR
This paper introduces GDAGS, a novel method for 3D Gaussian Splatting that uses gradient direction awareness to improve scene representation, reduce redundancy, and enhance rendering quality in complex scenarios.
Contribution
GDAGS employs gradient coherence ratio and dynamic weighting to control density adaptively, addressing over-reconstruction and over-densification in 3D Gaussian Splatting.
Findings
Achieves superior rendering quality on real-world benchmarks.
Effectively mitigates over-reconstruction and over-densification.
Constructs more compact and efficient scene representations.
Abstract
The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs when persistent large Gaussians cannot meet adaptive splitting thresholds during density control. This is exacerbated by conflicting gradient directions that prevent effective splitting of these Gaussians; (2) Over-densification of Gaussians occurs in regions with aligned gradient aggregation, leading to redundant component proliferation. This redundancy significantly increases memory overhead due to unnecessary data retention. We present Gradient-Direction-Aware Gaussian Splatting (GDAGS) to address these challenges. Our key innovations: the Gradient Coherence Ratio (GCR), computed through…
Peer Reviews
Decision·ICLR 2026 Poster
1. The proposed method achieves a good balance between performance and storage, aligns well with intuition, and enhances the overall usability of Gaussian Splatting models. 2. The authors conduct experiments across three benchmark datasets and compare against a wide range of strong baselines (NeRF, 3DGS, AbsGS, Pixel-GS, etc.). The inclusion of ablation studies and sensitivity analyses provides convincing evidence for the method’s robustness and interpretability. 3. The motivation of this paper
1. The overall performance(especially the LPIPS metric) is highly influenced by the Hyper parameters(α、β、p) which raises concerns about the generalization of the method. 2. Still about generalization ability. In section 4.3.4 the authors evaluate the proposed module combine with MCMC-3DGS and Compact-3DGS. However, noticeable performance changes appear mainly in the LPIPS metric and the SSIM metric on the Deep Blending dataset. Therefore, a qualitative analysis corresponding to these metric vari
This paper properly identifies the shortcomings of the prior method (AbsGS) and achieves the best control of the number of Gaussians during training by addressing this problem.
Please see Questions section for my major concerns. Presentation issues: - Math error in Equation 3. The expansion of $T_k$ is incorrect. It must be $\prod_{j=1}^{k-1} (1-\alpha_jG'_j(\textbf{x}'))$. - Typo in line 273: $(1 − C_i)^a$ → $(1 − C_i)^p$. - Clarify the unit of x-axis in Figure 5. It seems k (thousand).
1. The paper is well writen and easy to follow. 2. This paper targets a general and key component in the 3DGS pipeline and provides a simple yet effective solution. 3. The experimental results show promising performance improvements.
1. The proposed GDAGS introduces additional computational overhead during the optimization process. I believe it is necessary to report a comparison of training and testing times to better characterize the efficiency of the proposed method. 2. In Eq. (1), the variable \( i \) is not clearly defined. Does it refer to the Gaussian kernel? In addition, how is the number of views \( V \) determined in the experiments? Has the effect of different \( V \) values on performance been examined?
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