Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
Authors: Zheng Zhou
Yu-Jie Xiong
Jia-Chen Zhang
Chun-Ming Xia
Xihe Qiu
Hongjian Zhan
Venue: ICLR 2026 Poster (2026)
Keywords: Novel View Synthesis
3D Gaussian Splatting
Point-based Radiance Field
3D reconstruction
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: We present Gradient-Direction-Aware Gaussian Splatting (GDAGS), a gradient-direction-aware adaptive density control framework to address over-reconstruction and over-densification.

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 normalized gradient vector norms, which explicitly discriminates Gaussians with concordant versus conflicting gradient directions; and a nonlinear dynamic weighting mechanism leverages the GCR to enable gradient-direction-aware density control. Specifically, GDAGS prioritizes conflicting-gradient Gaussians during splitting operations to enhance geometric details while suppressing redundant concordant-direction Gaussians. Conversely, in cloning processes, GDAGS promotes concordant-direction Gaussian densification for structural completion while preventing conflicting-direction Gaussian overpopulation. Comprehensive evaluations across diverse real-world benchmarks demonstrate that GDAGS achieves superior rendering quality while effectively mitigating over-reconstruction, suppressing over-densification, and constructing compact scene representations.

Published as a conference paper at ICLR 2026
GRADIENT-DIRECTION-AAARR DENSITY CONTROL FOR 3D GAUSSIAN SPLATTING
Zheng Zhou', Yu-Jie Jia-Chhe Zhang', Chhnn-ingg Xia Xihe Qiu', Hongjian Zhan
SShanghai University of Engineering Science, Shanghai, China
"ast China Normal University, Shanghai, China
(m320123332, xiong, m325123603 cmxia @sues dasueseduu.. en qiuxihel 993 1938gmmal1 com, hjzhanêcee ecnu. wenu.edu.cm edu.cn
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 limi- tations in complex scenarios: (1) Over-reconstruution 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) iiverddnsifiiction Gaussians occurs in regions with aligned gradient aggregation, leading redundant component proliferation. This redun- dancy significantly increases memory overhead due to unnecessary data retention. We present Sradient-Directton Aware Gaussian Splatting (GDAGS) to address these challenges. Our key innovations: the Gradient Coherence Ratio (GCR), com- puted through normalized gradient vector norms, which explicitly discriminates Gaussians with concordant versus conflicting gradient directions; and a nonlinear dynamic weighting mechanism leverages the GCR to enable gradient-direction- aware density control. Specifically, GDAGS prioritizes confficting-graade gradient Gaus- sians during splitting operations to enhance geometric details while suppress- ing redundant concordant-directoo Gaussians. Conversely, in cloning processes, GDAGS promotes ooncoraan-ddirectoon Gaussian densification for structural com- pletion while preventing conflicting dirreetonnie Gaussian overpopulation. Compre- hensive evaluations across diverse real- world benchmarks demonstrate that GDAGS achieves superior rendering quality while effectively mitigating werrreeconstruction, suppressing deeifiinsiiiationn and constructing compact scene representations. Our code is available at httph::////// //gp:/u/thhbbb. com/ 22cq2
/ GDAGS
INTRODUCTION
Novel View Synthesis (NVS) constitutes a foundational challenge in computer graphics and vision, aiming to reconstruct 3D scenes from monocular images to generate photorealistic novel perspectives. While neural implicit representations, such as Neural Radiance Fields (NeRF) (Mildenhall et al.) 2021), have significantly advanced NVS by modeling radiation fields through multilayer perceptrons (MLPs) and volumetric rendering, these methods struggle to reconcile rendering quality (Verbin et al., al., 2022). Emerging as a novel paradigm, 3D Gaussian Splatting (3DGS) (Kerbl et al.) 2023) explicitly parameterizes scenes using adaptive
2022) with computational efficiency "Fridovich-Keis
Gaussian primitives. By integrating splat-based rasterization with adaptive density control strategies, 3DGS achieves real-time rendering of unprecedented fidelity while maintaining compact spatial representation.
However, the adaptive density control mechanism in 3DGS exhibits inherent limitations YYe et al. 2024. Huang et al., 2025) Zeng al., (205)). Specifically, 3DGS's densification criterion solely relies on the norm of view-space positional gradients, thereby overlooking the directional influence of gradients on the norm computation. During view-space positional gradient calculation for each
Corresponding author.

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Published as a conference paper at ICLR 2026
3DGS
PSSR: 21.2
2504 17175
4:29
Splating
Dausit
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Fail
271:31 5014
Density
GDAGS (Ouns)
Rendered
Ellipedd
siew of point
Figure 1: (a) illustrates the Gaussian ellipsoid splatting process, where arrows of different colors represent the gradient direction and magnitude of different Gaussians on the pixels. (b) shows the densification process of different methods. In 3DGS, a large Gaussian covering many pixels may fail to split because the combined gradient magnitude from different pixels falls below the threshold, leading to over-reconstruuciin as shown in the Rendered part of (c), which manifests as blurry areas. AbsGS forces all Gaussian gradients to be positive, causing the combined gradient magnitude from different pixels to increase significantly. This results in substantial rise in the number of splitting Gaussians, leading to over-densification as shown in the Top view of the point cloud part of (c), which appears as a large number of Gaussian point clouds outside the scene. Our proposed method utilizes gradient direction information to effectively control the splitting of large Gaussians and the growth in the number of small and medium Gaussians. (c) also presents the performance (performance metries are labeled in the top-left corner) and efficiency (FPS and total number of Gaussians in the scene, #G) of different methods.
Gaussian, the superposition of sub- sub-gradient components with consistent directions amplifies the gradient norm. Conversely, counter-directine sub uub-gradient components mutually attenuate, resulting in reduced gradient norms. These directional conflicts prevent some Gaussians from effective densification, causing the over-reconstrrucion phenomenon and perceptible local blurring /Zhang jet al. [2024b] Zhou et al. 2025). Meanwhile, Gaussians maintaining consistent gradient directions exhibit persistent densification tendencies, causing werr-ennifiiation phenomena that generate redundant Gaussian primitives and significantly increase memory consumption. Recent work like AbsGS (Ye et al. 2024) has proposed using absolute subgradient values to enforce positive gradients for all Gaussians, thereby avoiding subgradient directional conflicts and partially mitigating the over-reconstruucton issue, but this approach further exacerbates the over-densification phenomenon.
To address the aforementioned dual challenges, we propose a gradient-direction-n density control framework (GDAGS). This approach combines directional gradient information and gradient magnitude data to establish novel decision metric. The metric enables controlled densifica- tion of Gaussians with conflicting directional gradients while preventing redundant densification in directionally aligned cases. First, GDAGS calculates GCR to measure the directional consistency of subgradients for each Gaussian. The GCR approaching indicates highly consistent subgradient directions, whereas value approaching 0 signifies substantial directional conflict. Subsequently, GDAGS employs GCR to parameterize nonlinear dynamic weight w,, which independently weights the view-space positional gradients of each Gaussian to form new decision metric controlling densification behavior. During splitting operations, Gaussians exhibiting directional conflicts receive higher weights and are prioritized for splitting, while directionally consistent Gaussians obtain lower weights and are suppressed. Finally, GDAGS introduces a hyperparameter p to explicitly control the number of Gaussians participating in densification, thereby achieving indirect balance between performance and efficiency. Comparative results and efficiency analyses are summarized in Figure We summarize our contributions follows:
adaptive
We propose GCR as a gradient direction consistency measure for adaptive density control to identify Gaussians with consistent and divergent directions.
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We propose nonlinear dynamic weighting mechanism to independently weight the Gaus- sians in the scene, promoting correct densification and suppressing abnormal densification of the Gaussians.
Evaluated on three real-world datasets spanning 13 scenes, our method demonstrates superior visual quality while having a compact spatial representation.
2 RELATED WORK
2.1 NOVEL VIEW SYNTHESIS
Novel View Synthesis has evolved through two interconnected trajectories: advancements in scene representation paradigms and optimizations of rendering efficiency. The field was revolutionized by NeRF (Mildenhall et al. 202Tp. which introduced MLP-based implicit neural representations to map spatial coordinates to radiance and density values, achieving photorealistic results through volumetric rendering. However, NeRF's computational demands spurred research bifurcating into quality refinement and efficiency optimization. Approaches like Mip-NeRF (Barron et al. (021) enhanced geometric stability using conical ray encoding and distortion regularization, while DVGO (Sun) al. 2022) and Instant-NGP (Müller et al. 2022 leveraged explicit 3D grids and hash encoding to achieve near-real-time rendering. This progression culminates in 3D Gaussian Splatting (Kerbl et al.) 2023), which redefined the paradigm by combining explicit Gaussian primitives with differentiable rasterization. By parameterizing scenes as adaptive 3D Gaussians, 3DGS bridged the gap between implicit continuity and explicit computational efficiency, achieving rendering speeds two orders of magnitude faster than NeRF while maintaining visual parity with state-of-the-art methods like Mip-NeRF360 (Barron al. 2022).
2.2
3D GAUSSIAN SPLATTING
The rapid adoption of 3DGS has driven innovations addressing its inherent limitations (Cheng jet al.] 2024; Hamdi et al., 2024 Kheradmand et al., 2024; Lee et al.,
2024 Li et al. (025b).
Early improvements focus geometric fidelity: Mip-Splatting (Yu et al. 2024a) integrates multi- scale primitives and low- low-pass filtering to mitigate aliasing, while GOF (Yu et al. 2024b) introduces composite gradient metrics optimize Gaussian distributions. Subsequent methods explore structural constraints, such as 2DGSS (Huang et al., 2024) planar surface projections and RadeGSs (Zhang et al. 2024a) normal map-guided refinement. Frequency-awee approaches like FreGS (Zhang et al.) 2024b) employ spectral regularization to enhance eigh-ffreuency detail recovery. whereas HoGS (Liu et al. 2025) utilizes homogeneous coordinate reparameterization to improve distant object reconstruction. Despite these advances, critical challenges persist in adaptive density control. AbsGS Ye et al. 2024) enforces gradient direction uniformity to resolve over-reconstruction but indiscriminately amplifies outliers, while PixeIGS (Zhang et al.) 2024c) trades memory efficiency for geometric precision by weighting gradients via pixel coverage
strategy incurring prohibitive storage costs and frame rate penalties. ReAct-GS (Cheng et al. 2025) proposes densification and reactivation mechanism based on importance perception to improve rendering quality, while PSRGS (Li ct al., 2025a) solves the problem of high-frequency detail loss by identifying geometric and texture coupling regions. These methods inevitably increase model complexity by introducing additional modules or strategies. Our work addresses these unresolved trade-offss through directional gradient awareness, systematically balancing precision, efficiency, and memory constraints.
3 METHOD
Section 3.1 establishes the methodological motivation for GDAGS, and Section 3.2 outlines 3DGS fundamentals. Sections 3.3 detail GDAGS' core components. with full algorithmic implementation provided in Appendix The complete pipeline is shown in Figure 2
3.1
PRELIMINARY
3DGS represents scenes using a set of learnable anisotropic 3D Gaussian primitives. Each Gaussian is parameterized by position μ TR3 opacity E [0, 1]. covariance matrix Σ R3x3
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Figure 2: The pipeline of GDAGS. First, for each Gaussian, GDAGS computes the GCR to quantify the directional coherence of its subgradients. Subsequently, this GCR metric is mapped through nonlinear dynamic weighting function generate per- Gaussian gradient weights, which modulate the view-space positional gradient magnitudes and produce a refined decision metric. Finally, this decision metric is compared against a predefined threshold to dynamically regulate densification.
and Spherical Harmonics (SH) (Arfken al. 2011) coefficients for view-dependent color. The covariance matrix Σ is decomposed into a rotation matrix It and a scaling matrix to ensure positive semi-definiteness:
E RSS R
where is diagonal matrix parameterized by vector (R_)², and R is rotation matrix parameterized by a quaternion g E IR., The Gaussian distribution is defined as:
G(x) exp (-2(x
1m)
where x E R3 an arbitrary position.
During rendering, 3D Gaussians are projected into 2D camera space as G', with pixel color C(x') computed via boga-bbenndig:
K
C(x') = Σ Cx'' T. = (1-
(3)
k=1
Here, K denotes the count of Gaussians influencing the pixel, represents the view-dependent SH-based color, and Og combines opacity with the Gaussian's spatial contribution at x'.
To address sparse input from structure- cenecture-from--oee from-motion (SfM) (Schonberger & Frahm 2016). densifica- tion strategy is employed. Under-reconstruutted regions (missing geometric features) are addressed by cloning Gaussians, increasing their number and volume. Over-reconstruuted regions (large Gaussians covering small areas) are split into smaller Gaussians, maintaining volume but increasing count. Densification decisions are based view-space gradients. For a Gaussian G3 with projection μ under viewpoint & and loss Lg, the average gradient magnitude is:
1
Σ
M
ÖLk Outi
2
aLk Out
2
+
M
1=1
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where M is the number of viewpoints involved in training.
Split Gaussians into smaller components V "r L,L > and Eg > Ts. Clone Gaussians to preserve local density if Vig I > Tp and EiD S Ts.
3.2 MOTIVATION
The density control in 3D Gaussian Splatting primarily depends comparing the norms of view- space positional gradients against predefined thresholds. However, the directional properties of these gradients implicitly influence their magnitudes- undesirable effects such as local blurring and excessive Gaussian proliferation, which ultimately im- pair rendering quality. To overcome these issues, we introduce novel decision metric that explicitly incorporates subgradient direction through nonlinear weighting function, effectively addressing both over-reconstruution and wee-densification. Specifically, during splitting operations -which target larger Gaussians-we we use the GCR to guide the weighting strategy. Gaussians with smaller GCR values, indicating directional conflicts, receive an amplified decision metric via nonlinear weighting, thus prioritizing their densification. In contrast, those with larger GCR values, reflecting directional consistency, are assigned a diminished metric and suppressed. During cloning operations, which affect smaller Gaussians, the logic is reversed: low-GCR Gaussians are suppressed, while high-GCR Gaussians are promoted. This is because high-GCR clones can smoothly propagate along the gradient direction, whereas low-GCR clones tend to accumulate locally without meaningful displacement. By systematically integrating both gradient direction and magnitude into the densification criterion, GDAGS optimizes the density control mechanism. This approach only mitigates key limitations of standard 3DGS but also significantly reduces the number of Gaussians required and corresponding memory usage.
attenuating or amplifying them-leading to
3.3 GDAGS
3.3.1
DIRECTIONAL CONSISTENCY MEASUREMENT
For each Gaussian observed across V views, compute directional consistency metric GCR as C₁:
11 Eppiree Vi Vi.pinettle l12
C: Episee V Vipizzellle 122 +
where pixel denotes pixel in the 2D space, and V Sppizel E IR2 represents the subgradient component projected onto each pixel in view v. According to the Cauchy-Schwarz Inequality 0 || Piiett V. ipppinet ll2 S Epizel V Vi,peaeell22 it can be seen that the value of C, is strictly con- trolled between [0, and GCR isolates the influence of gradient amplitude and only represents the consistency of gradient direction. This formulation captures:
High Consistency (C, + 1): Gradient directions are consistent, their magnitudes effectively accumulate.
Low Consistency (C, - 0): Conflicting gradient directions result in mutual cancellation of magnitudes.
The denominator Epizel V Vi,pinaall ll2 captures the total gradient activity agnostic to directional variations, while the numerator Epiret V. 2izzizzel22 quantifies the net directional alignment. Due to the fact that GCR calculates subgradients for each pixel in multiple views, GDAGS works under clear assumption that most Gaussian distributions in the scene receive sufficient gradient information.
3.3.2 NONLINEAR DYNAMIC WEIGHT DESIGN
The core of our adaptive density control lies in transforming the GCR into a dynamic weight that modulates the gradient magnitude used in densification decisions. A naive linear weighting scheme proves insufficient, as it lacks the expressive power to aggressively suppress high-consistency Gaussians while sensitively amplifying those with critical directional conflicts. To address this,
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introduce nonlinear weighting function designed with two key objectives: strongly attenuating the contribution of Gaussians with high GCR values to prevent redundant densification in well-defined regions. And strategically amplify the influence of Gaussians with intermediate to low GCR values, prioritizing them for densification to resolve directional conflicts and enhance geometric detail. We design nonlinear weighting function defined as:
+ B (1-)..))
where C, is the gradient coherence ratio for Gaussian i computed via Equation [ a is the fundamental inhibitory factor that suppresses Gaussian densification with consistent gradient directions during the splitting process, and suppresses Gaussian densification with inconsistent gradient directions during the cloning process. |3 acts as the amplification factor, scaling the weight for Gaussians with strong directional conflicts, and p governs the steepness of the exponential suppression, rapidly decreasing the weight for Gaussians with high directional consistency.
The exponential form is motivated by the need for strong, monotonic decay that effectively neutral- izes the influence of Gaussians whose gradients are already aligned. This prevents the unnecessary splitting or cloning that leads to over-densification. The power-law form in the amplification region, (1 - (1,C,)", is chosen because is more sensitive to changes in when consistency is low compared to linear function. This sensitivity is crucial for identifying and prioritizing Gaussians that are large and cover areas with complex geometry (and hence conflicting gradients), which are most in need of splitting.
The modulated gradient norm for Gaussian then computed as:
j. L = W; V L.
This refined metric replaces the original gradient norm in all densification decisions, enabling consistent aaadieentdirection-avaee control during optimization. The weighting function is applied strategically according to the densification operation: during splitting, the weight wy applied directly to prioritize the fragmentation of large Gaussians exhibiting directional conflicts. In cloning operations, the inverse policy (1/u) (1/w:) is adopted to encourage the propagation of small Gaussians with high GCR along surface directions, while suppressing those with low GCR to prevent chaotic local accumulation. Comprehensive ablation studies in Section connfirm the superiority this nonlinear weighting strategy over linear alternatives, and a sensitivity analysis of parameters p and is provided in Section
4 EXPERIMENTS
4.1 SETUP
Datasets. Similar the 3DGS dataset, select all 9 scenes introduced in MIP-NERF360 (Barron et al. 2022). including indoor and outdoor scenes, Train and Truck scenes in Tanks& Temples HHed man et al. 2018). and drJohnson and playroom scenes in Deep Blending (Knapitsch et al. 2017))
Baselines. The proposed method is benchmarked against three categories of approaches: fast NeRF
frameworks (Plenoxels (Fridovich-Keil al. (022)), Instant-NGP (Müller al., (022)), state-of-the- art NeRF- models MMip-NeRF360 (Barron et al. 2022)), and 3DGS variants including the widely adopted 3DGS (Kerbl et al. 2023). Taming 3DGS Mallick al. (024)) and mini-splatting (Fang & Wang 2024), over-reconstruction-foouuce -occused AbsGS YYe et al. (2024), and Pixel-GS (Zhang et al. 2024c), ensuring comprehensive evaluation across datasets.
Implementation Details. Our experimental setup follows the original paper parameter settings of 3DGS, with densification performed every 100 iterations, stopping densification after 15k iterations and completing training at 30k iterations. The suppression factor in the weight function is set to 0.8, the amplification factor is set to the size threshold of 25, and the hyperparameter p is set to 15. single NVIDIA 4090 GPU with 24GB of memory. We report the
All experiments conducted view metrics PSNR, SSIM, and LPIPS (Zhang et al. 2018), and the memory used to store Gaussian parameters demonstrate the trade-off between performance and efficiency.

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Table Quantitative comparison on three datasets. SSIM and PSNR are higher-the-better, LPIPS. is lower-the- For fair comparison and to balance the trade-off between overall quality and memory consumption, we train these datasets with the same settings as 3DGS. All methods use the same training data for training. The best score
second best score are red and orange, respectively.
Datasets
Mip-NeRF300
Tanks& Temples
Deep Blending
Methods
SSIMI
LPIPS]
Meml
SSIM1
PSNR1
LPIPS.
Mem
SSIM1
PSNR
LPIPS].
Mem
Planoxels
0,626
23.08
0.463
2.1GB
0.719
21.08
0.379
2.3GB
0.795
23.06
0.510
2.7GB
INGP
0.671
25.30
0,371
13MB
0.723
21.72
0.330
13MB
0.797
23.62
0.423
13MB
Mip NeRF360
0,792
27.69
0.237
866BB
0.759
22.22
0.257
86MB
0.901
29.40
0.245
8.6MB
3DXSS
0.855
27.21
0.214
734MH
0.841
23.14
0.183
4TTMB
0.503
29.41
0.243
676MH
PixeI-GS
0.832
27.72
0.178
1.2GB
0.853
23.74
0.150
L,0GB
0.896
28.91
0.248
L.GB
AbaGS
0.820
27.49
0.191
728MB
0.853
23.73
0.162
304MB
0.902
29.67
0.236
444MB
Taming 3DGS
0.822
27.79
0.205
735MB
0.851
24.04
0.170
4MMMB
0.907
30.14
0.235
676MB
mini-splatting
0.822
27.34
0.217
225MB
0.835
23.18
0.202
130MB
0.908
29.98
0.253
70MB
GDAGS (Ours)
0.839
0.145
515MB
0.854
23.79
0.165
226MB
0.905
29.70
0:235
384MB
213
GT
GDAGS
3DGS
AbxGS
PixiGGS
Figure 3: Qualitative comparisons of different methods scenes from Mip-NeRF360, Tanks& Temples and Deep Blending datasets. Enlarged images are displayed in the bottom right corner.
4.2 COMPARISON
Quantitative Results. We report the quantitative results of the experiment in the Table which indi- cate that method produces comparable results in most cases. It worth noting that method has less memory at performance close to or better than SOTA. Compared to Pixel-GS, GDAGS achieves comparable or superior rendering performance with only 20%-50% of the memory consumption. This, combined with its comparison against 3DGS, demonstrates that GDAGS effectively addresses over-densification through nonlinear dynamic weights that suppress redundant Gaussians during densification. By adaptively controlling density, GDAGS enables scene Gaussians to converge toward compact spatial representations.
Compared to AbsGS, which enforces non-negative gradient components to resolve directional con- flicts, this approach introduces new issue: it amplifies the influence outlier gradients arising from noisy suboptimal regions. As result, it triggers excessive and often unnecessary splitting, leading to over-densification. higher memory consumption, and noisier renderings-winn improvement in visual quality. In contrast, Pixel-GS employs pixel-coverage weighting mechanism that enhances spatial adaptation. However, fails to account for directional information gradients. This omission means it still encourages densification in areas with large yet directionally consistent gradients, resulting in redundant Gaussians and elevated memory costs. Our method addresses these limitations by leveraging directional coherence to actively suppress such redundant densification.
a proportional
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Qualitative Results. We present NVS results of GDAGS and baseline methods in Figure demon- strating that our approach achieves high-quality visual fidelity across all scenes while effectively mitigating localized blur and detail loss. Comprehensive quantitative and qualitative results are presented in the Appendix D
4.3 ANALYSIS
Table Ablation experiment three datasets. SSIM and PSNRÍ higher-the-better; LPIPS is lower-the-better. The best score and second best score are red, and orange, respectively.
Datasets
Mip-NeRF360
Tanks Temples
Deep Blending
Methods
SSIM
PSNR1
I.IPS.
Mem.
SSIM1
PSNR1
LPIPSL
Mem
SSIM1
PSNR
LPPPS].
Memj
3DGS
0.815
27.21
0.214
734MB
0.841
23.14
0.183
411MB
0.903
29.41
0.243
676MB
GDAGS-L
321MB
29.62
0.241
GDAGS-S
0.819
27.52
0.240
441MB
0.346
23.54
0.178
195MB
0.905
29.67
0.238
328MB
GDAGS-C
305MB
29.70
GDAGS (Ours)
0.839
28.02
0.145
515MB
0.852
23.79
0.165
226MB
0.905
29.70
0.235
388MB
4.3.1 ABLATION STUDY
We perform ablation studies on the proposed weighting strategy by training three model variants: GDAGS-S (applying weighting only during splitting), GDAGS-C (only during cloning), and GDAGS- L (using a linear weighting function u, = 2 C,). These are compared against the original 3DGS and the full GDAGS model. Results are summarized Table The study reveals that split-oriented weighting (GDAGS-S) effectively improves SSIM and LPIPS while reducing memory usage. Clone- oriented weighting (GDAGS-C) enhances reconstruction fidelity (PSNR) but at the cost of increased memory consumption. Integrating both strategies in the full GDAGS model achieves optimal balance between performance and efficiency. Notably, the nonlinear weighting function GDAGS significantly outperforms its linear counterpart (GDAGS-L), as it more effectively translates gradient direction coherence into adaptive control signals. This nonlinear mapping provides superior sensitivity directional conflicts and consistency, leading to stable optimization and higher-quality results across all metrics compared to the linear approximation.
SSIM Comparison
PSNR Comparion
0.80
0.80
10
PSNN (EF)
0.80
0.80
22
100
VValue
LPIPS Comparison
Menorry Usage Comparison
Menoory (M4B)
000
LIPPS
0.3
0.30
334
315
224
300
200
120
17.
22.
310
Volue
0 Valln
bonsai
bicyclo
Train
Truck
Figure 4: Performance of different hyperparameters p in multiple datasets.
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4.3.2 PARAMETER SENSITIVITY ANALYSIS
sensitivity analysis is performed on hyperparameters p and B, with detailed results for included in the Appendix C
The study shows that exerts a stronger influence on both rendering performance and computational efficiency. As demonstrated in Figure we test p across range of values: 10, 15, 20, 25, and 30. As previously explained, determines the proportion of Gaussians selected for densification within the adaptive control mechanism. Increasing p restricts densífication to fewer Gaussians, which reduces memory usage but also compromises rendering quality. In contrast, smaller p expands the candidate set for densification, enhancing visual performance at the expense of greater memory overhead. These findings underscore the critical role of p in balancing the trade-off' between efficiency and reconstruction quality.
2000
Deseticatoon Round
Dottiiiciio Aound
Figure 5: Visualization of different densification methods during the training process in bicycle sense.
4.3.3 DENSIFICATION PROCESS ANALYSIS
We quantified the Gaussian split/clone counts per densification round and the total Gaussian popula- tion dynamics during training in Figure The experimental results demonstrate distinct behavioral patterns: In cloning operations, all three methods exhibit consistent trends, with 3DGS achieving the highest clone count due to its standard threshold. AbsGS, employing twice the cloning threshold of 3DGS, yields the lowest clone numbers, whereas GDAGS suppresses directionally inconsistent clones through gradient alignment constraints, resulting in intermediate cloning activity. GDAGS further ensures localized replenishment of directionally consistent clones while mitigating redundant accumulation of geometrically confficting clones. For splitting operations. AbsGS generates exces- sive splits by indiscriminately utilizing gradient magnitudes, whereas 3DGS underperforms due to gradient direction conflicts. GDAGS effectively filters splittable Gaussians through adaptive criteria. Regarding total Gaussian counts, GDAGS exhibits a more stable trajectory and faster convergence compared to 3DGS and AbsGS, validating its optimized densification strategy in balancing geometric fidelity and computational efficiency.
Table Generalization analysis on three datasets. SSIM and PSNR are higher-the-better, LPIPS..
is lower-the-beree better The best score is red.
Datasets
Mip-NeRF360
T
Deep Blending
Methods
SSIM
PSNR
I.PIS
SSIM
PSNR1
LPIPS
SSIM
PSNR1
LPIPS.
MCMC-3DGS
0.900
29.89
0.190
0.860
24.29
0.190
0.890
29.67
0.320
MCMC-GDAGS
0.900
29.80
0.150
0.860
24.19
0.150
0.910
29.59
0.240
Compact-3DGG
0.798
27.08
0.247
0.831
23.32
0.201
0.901
29.79
0.258
GoApS
0.804
27.02
0.227
0.834
23.23
0.191
0.902
29.74
0.250
4.3.4 GENERALIZABILITY ANALYSIS
To demonstrate the generalizability of the proposed densification control strategy, we generalize our GDAGS module to two other advanced 3DGS frameworks: MCMC-3DGS (Kheradmand et al. 2024) and Compact-3DGs (Lee et al. 2024). As quantitatively shown Table the integration of GDAGS consistently improves the performance of both baselines, yielding superior results in terms of SSIM

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and LPIPS. This demonstrates the broad applicability and effectiveness of craddeentdiirction aware densification control in enhancing reconstruction quality across different 3DGS architectures. As shown in the qualitative results of Figure the model integrated with GDAGS performs better object surfaces and geometric edges, such as room floors, wardrobe gaps, etc.
LIIPE
GIDAGS
MICAGI
WENTies
Compatt
Compact
Compact
Compact-3DDS
Figure 6: Qualitative analysis of GDAGS integrated in MCMC-3DGS and Compact-3DGSS.
Table Efficiency analysis of GDAGS and baseline models. TT(s) in the table represents training time (in seconds). The best score is red.
Datasets
Mip-NeRF360
Tanks& Temples
Deep Blending
Methods
Training time(s)
FPST
Training time(s)
FPS1
Training time(s)}
FPS1
3DGS
1594
134
912
154
1344
137
Pixel-GS
1811
89
1450
92
1640
90
AbsGS
1571
111
621
125
937
142
Mini-splatting
1320
386
896
432
1095
416
GDAGS
1140
188
555
220
898
196
4.4 EFFICIENCY ANALYSIS
We report the training time and inference time (FPS) of GDAGS compared to other baseline models in Table 4 As the results demonstrate, GDAGS achieves the fastest training convergence across all datasets. This efficiency stems from our pradient-direction-vaaas
densification control, which strategically prioritizes the most impactful splitting and cloning operations. By avoiding redundant densification steps that contribute little to reconstruction quality, GDAGS not only produces a more compact scene representation but also reduces the total computational cost of training. Regarding the computational overhead of GCR, we argue it is negligible for two key reasons. First, the calculation of the GCR is computationally lightweight, involving only simple vector norms and summations on pre-existing gradients. Second, and more importantly, the GCR-driven densification control leads to far more compact scene representation, often reducing the final number of Gaussians by hundreds of thousands to millions compared to 3DGS. This reduction directly accelerates subsequent optimization steps.
CONCLUSION
This paper proposes GDAGS, dynamic adaptive Gaussian splatting framework that addresses critical challenges in 3D scene reconstruction, including over-densification. over-reconstruction. and memory inefficiency. By introducing the directional consistency metric (GCR) and nonlinear dynamic weighting, GDAGS enables direction-aware densification control: GCR discriminates between directionally consistent/divergent Gaussians, while dynamic weights independently regulate densification the per-Gaussian level. This dual mechanism effectively suppresses redundant Gaussian prolifcration, climinates outlier distributions, and drives compact spatial representations without introducing additional heuristic densification thresholds.
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A ALGORITHM
Algorithm Gradieen--irrccion-Aaaae Adaptive Densification
Require: Gaussian set 9, training views V, initial thresholds 5
Ensure: Optimized Gaussian set G
1: Initialize gradient buffers [V..} <- 0, indicate the gradient of the i-th Gaussian at view angle v
2: for each iteration
to I" do 3: for each view v E Vo
4: Render 9 via differentiable splatting
5: Compute 2D gradient map Zpize! Vi S.pizel and Σ Epiret V
1.pl22
Accumulate V for visible Gaussians end for for each Gaussian 9i G do
6:
7:
8:
9:
Compute C₁ via Eq. equation
10:
Calculate w; using Eq. equation
11:
end for
12:
Compute V splits V Vllone
13:
Split if V split Tp & 23D
14:
Clone if V clonee > Tp & Enn S T
end for
B WEIGHT FUNCTION RESPONSE CURVE ANALYSIS
As shown in Figure 7 we systematically analyzed the influence of parameters and 3 in the weight function. When 8 is fixed, increasing the value of p will cause the weight curve to in C,- When the value is smaller, it becomes steeper, which means stronger punishment is imposed on Gaussian beams with inconsistent directions. When p = 15 is fixed, 3 mainly controls the overall magnitude of the weight function. These observation results are consistent with the quantitative analysis in the main text, verifying the rationality of parameter design.
Fixed f Vary
Vary
2-
F11
155
(111
05
Figure 7: Corresponding curves of weights for different hyperparameters p and B.
C
ANALYZE
C.1 HYPERPARAMETER SENSITIVITY ANALYSIS
The hyperparameters beta were set to 15, 20, 25, 30, and 35 for sensitivity analysis, and the results shown in Figure g The results show that the setting of beta has relatively small impact overall performance, and increasing beta can slightly improve performance and slightly increase the required storage space.
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Comparison
Comparison
PSNR (cB))
SSIM
0.05
Value
VValc
LPITS Compurison
Manooy Usage Compurison
0.20
Memory (MBB)
19:0
800
215
200
33.5
@ Value
VVaie
bonsai
---..
hicycle
-
Train
-*..
Truck
Figure 8: Performance of different hyperparameters 3 in multiple datasets.
C.2 DENSIFICATION PROCESS ANALYSIS
Figure 9 quantifies the Gaussian split/clone counts per densification round and the total Gaussian population dynamics during training. The results reveal distinct behavioral patterns across methods: In cloning, all three methods show consistent trends. 3DGS produces the most clones under its standard threshold, while AbsGS yields the fewest due to its doubled threshold. GDAGS suppresses directionally inconsistent clones via gradient alignment, resulting in intermediate numbers and ensuring localized replenishment without redundant accumulation. For splitting, AbsGS over-splits by using gradient magnitudes indiscriminately, whereas 3DGS under-splits due to gradient conflicts. GDAGS effectively filters splittable Gaussians with adaptive criteria. Overall, GDAGS exhibits more stable trajectory and faster convergence in total Gaussian counts than 3DGS and AbsGS, confirming its balanced densification strategy between geometric fidelity and efficiency.
Terdee
1930
1910
1000
100
Deastiicsss Reued
Denccfaatse Rouzd
Desstherte Rouud
Figure 9: Visualization of different densification methods during the training process in the flowers sense.
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Table 5: Comparison of training time between power function and exponential function. The best result is bold.
Methods
Mip-NcRF360
Tanks& Temples
Deep Blending
GDAGS
1140s
555s
898s 976s (+8.7%
GDAGS-E
1200s (+5.2%)
612s (+10.3%)
Table 6: Comparison of performance between power function and exponential function. The best
result bold.
Datasets Methods
Mip-NeRF360 PSNR1
Tanks& Temples
Deep Blending
SSIM
LPIPS
SSIMI
PSNRT
LPIPS
SSIM
PSNR
LPIPS.
GDAGS
0.839
28.02
0.145
0.852
23.79
0.165
0.905
29.70
0.235
GDAGS-E
0.837
27.96
0.146
0.851
23.62
0.165
0.903
29.60
0.237
C.3 NONLINEAR DYNAMIC WEIGHTED FUNCTIONS ANALYSIS
Nonlinear functions usually represented power functions ((1 C.) and exponential functions (e-PC). GDAGS chooses to use the power function form for two main considerations. Firstly, from the perspective of computational load, the caleulation of power functions is very efficient, while the calculation of exponential functions is more complex. The running time of torch.exp in Py PyTorch environment is longer than torch.pow. We construct a model consisting of exponential functions and named it GDAGS-E. E. We conduct additional exponential function ablation experiments, the training time and performance are reported in the Ta and Table The results show that using exponential functions increases training time by 5%- 10% compared to power functions.
Then, we analyze the properties of exponential and power functions themselves. We consider two candidate forms for the nonlinear weighting component, power function fp(x) (1 z)" and exponential function (x) e -pz To compare their sensitivity characteristics, we analyze the absolute values of their derivatives: (x) = p(1 p-) and (x)) = pe pu These derivatives represent the rate at which each weighting function responds to changes in gradient coherence. For a power function, it is monotonically decreasing function over the domain [0, 1]. with value range of fp E [0, 1). For exponential function, is a monotonically decreasing function over the domain x [0, 1]. with value range of fp E [pe-P, 1]. We let f(x)
x; e p=p whose derivative is f = plc p x² -,- and the corresponding curve of the function is shown in the Figure 10 In the domain, f(0 = 0. f(1) > O, the derivative is first less than 0 and then greater than Ty keeps approaching 0. This indicates that the power function curve is
0, and the zero point always below the exponential function curve and as p increases, the exponential function continues to approach the power function. When 0 < x < Ho. I' < O indicates that the power function decreases faster than the exponential function the [0, xo] interval. When 2i < x < J > indicates that the power function decreases more smoothly than the exponential function in the [ro, 1] interval. This is consistent with our design goal of selecting only Gaussians with significant gradient direction conflicts and maintaining consistent suppression for Gaussians with consistent gradient directions.
C.4 ANALYSIS OF ABNORMAL BEHAVIOR OF DIFFERENT MODELS ON DATASET DEEP BLENDING
We observed that the performance of GDAGS on the Deep Blending dataset is slightly lower than that of Mini plating, and the number of Gaussians in Mini plating significantly less than that of GDAGS. The performance metrics reconstructed on other datasets show the same trend as the Gaussian number, while the opposite trend is observed on the Deep Blending dataset. Based on this phenomenon, we propose hypothesis that the relatively simple geometric shapes and textures of the Deep Blending dataset make them more prone to overfitting when modeling with excessive Gaussian numbers. To test this hypothesis, introduced Gaussian dropout mechanism into GDAGS during training, creating variants dubbed EDAGS-DROD (random dropout) and Sopacity-weighee dropout). The results reported in Table 7 The performance of GDAGS-DROP
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Function: f= (1 x)p -P
0.010
0.005
Value
0.010
-0.015
-0.020
10
-0.025
-0.030
Derivative: f= ple pi -11 x1P 21
0.10
p 15
0.05
25
0.00
-0.05
Derivative
0.15
-0.25
0.2
0.6
x
Figure 10: The function curve of the difference between power function and exponential function.
initially improves with a moderate dropout rate before declining, which confirms that overfitting is relevant factor on this dataset. The optimally regularized variant, HIDAGS-ODRO... all Mini-splatting configurations in SSIM and PSNR. At the same time, we consulted some 3DGS related work, and one of the papers, ControIGS (Zhang et al.) 2025), studied this situation. ControlGS proposes that the number of Gaussians increases, scene reconstruction will go through four stages: underfitting, effective state, saturation, and overfitting. ControlGS also uses pruning to keep the model in effective state. Notably, ControIGS exhibits a similar cross-dataset performance pattern: underperforms against other baselines on complex datasets like Mip-NeRF- and Tanks& Temples but excels Deep Blending, mirroring findings.
surpasses
Table 7: Performance comparison of GDAGS-DROS with Mini- Mini-splating-P (partial pruning) and Mini-splatting-D (only perform densification without pruning) the Deep Blending dataset.
Methods
Gaussians (M)
SSIM
PSNR
LPIPS
Mini-splatting
0.35
0.908
29.98
0.253
Mini-splatting
1,55
0.906
29.92
0.230
Mini-splatting-D
4.63
0.906
29.88
0.211
GDAGS
1.71
0.905
29.70
0.235
GDAGS- ODROP-5%
0.74
0.909
30.03
0.241
0.74
0.908
29.91
0.242
DOAGG-DRRP-IT
0.58
0.908
29.88
0.247
GDAGS- EDAGS-DROP-200
0.32
0.906
29.78
0.255
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C.5 GRADIENT SPARSE SCENE ANALYSIS
To analyze the behavior of GDAGS and baseline models in sparse gradient regions, we trained GDAGS-H, 3DGS-H, AbsGS-H, and Pixel-GS-H models respectively, where H represents using 50% of the training views to increase the range of sparse gradient regions. As shown in Figure IT GDAGS has fewer artifacts and better geometric edges in gradient sparse areas such as the sky, walls, and ground. Although the assumption of GDAGS is that Gaussian needs to receive sufficient gradient information, GDAGS still works well in sparse gradient regions.
3DGS
GDAGS
AbsGS
Pixel-GS
Figure 11: Qualitative results of GDAGS and baseline models in sparse views.
C.6 ANALYSIS AND GUIDANCE OF HYPERPARAMETERS
First, emphasize that all experimental results obtained using single, fixed set of hyperpa- rameters (o 0.8, 3 = 25, = 15) across all scenes and datasets, without any scene-specific tuning. The consistent improvements in rendering quality (PSNR, SSIM, LPIPS) and memory ef- ficiency demonstrate the strong generalization capability and robustness of GDAGS under fixed configuration.
Second, the roles of a. 3, and are well-defined and interpretable:
1. When the GCR approaches 1, the weight converges to a. Since Gaussians with high GCR typically exhibit large gradient magnitudes, (set between and 1) acts suppression factor to prevent over-densification in well-reconstructed regions.
2. When GCR approaches 0, the weight becomes a + B. These Gaussians often have small gradient norms due directional conflicts. To promote their densification, we / match the max_screen, size value from the original 3DGS implementation, ensuring compatibility and intuitive scaling.
3. The exponent p controls the number of Gaussians selected for densification. Its effect predictable: increasing restricts densification to fewer Gaussians, reducing memory usage at a potential cost to reconstruction quality; decreasing p has the opposite effect. In practice, and 3 can remain unchanged with minimal impact performance, and the value of can be adjusted according to different requirements (high rendering accuracy or low memory consumption).
Furthermore, sensitivity analysis confirms that GDAGS maintains stable performance across wide range of p (10-30) and (15-35) values, reinforcing its generalizability without demanding extensive hyperparameter search.
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D DETAILED RESULTS
The detailed performance metrics across total 13 different scenes from the three datasets are shown in Tables 8 9
13 and while efficiency metries are presented in Tables I1 and [5] Render quality comparisons are demonstrated in Figures [2] and [3] where significant differences are highlighted with red boxes and enlarged in the bottom-right corners. Figure 14 compares over- densification phenomena, with FPS values and the number of Gaussians annotated in the top-left corners.
Table 8: Per-scene quantitative results (SSIM) from the Mip-NeRF360.
bicycle
flowers
garden
stump
trechill
room
counter
kitchen
bonsai
Plenoxels
0.496
0.431
0.606
0.523
0.509
0.841
0.759
0.648
0.814
INGP-Basc
0.491
0.450
0.649
0.574
0.518
0.855
0.798
0.818
0.890
INGP-Big
0.512
0.486
0.701
0.594
0.542
0.871
0.817
0.858
0,906
Mip-NeRF360
0.685
0.583
0.813
0.744
0.632
0.913
0.894
0.920
0.941
3D-GS
0.771
0.605
0.868
0.775
0.638
0.914
0.905
0.922
0.938
AbsGS-0004
0.782
0.613
0.870
0.784
0.626
0.920
0.908
0.929
0.944
PixeI-GS
0.766
0.627
0.862
0.784
0.638
0.929
0.921
0.936
0.951
GDAGS
0.798
0.641
0.879
0.796
0.638
0.951
0.928
0.954
0.961
Table Per-scene quantitative results (PSNR) from the Mip-NeRF360.
bicycle
flowers
garden
stump
trechill
counter
kitchen
bonsai
Plenoxels
21.912
20.097
23.495
20.661
22.248
27.594
23.624
23.420
24.669
INGP-Base
22.193
20.348
24.599
23.626
22.364
29.269
26.439
28.548
30.337
INGP-Big
22.171
20.652
25.069
23.466
22.373
29.690
26,691
29.479
30.685
Mip-NcRFF30
24.37
21.73
26.98
26.40
22.87
31.63
29.55
32.23
33.46
3D-GS
25.246
21.520
27.410
26.550
22.490
30.632
28.700
30.317
31,980
AbsGS-0004
0000
25.373
21.298
27.579
26.766
22.074
31,582
28.968
31.774
32,283
PixeI-GS
25,263
21.677
27.393
26.938
22.343
31.952
29.270
31.950
32.687
GDAGS
25.738
21.711
27.839
27.064
22.217
32.566
29.531
32.698
32.839
Table 10: Per-scene quantitative results (LPIPS) from the Mip-NeRF360,
bicycle
flowers
garden
stump
treehill
room
counter
kitchen
bonsai
Plenoxels
0.506
0.521
0.386
0.503
0.540
0.4186
0.441
0.447
0.398
INGP-Base
0.487
0.481
0.312
0.450
0.489
0.301
0.342
0.254
0.227
INGP-Big
0.446
0.441
0,257
0.421
0.450
0.261
0.306
0.195
0,205
Mip-NeRF360
0.301
0.344
0.170
0.261
0.339
0.211
0.204
0.127
0.176
3D-GS
0.205
0.336
0.103
0.210
0.317
0.220
0.204
0.129
0.205
AbsGS-0004
0.186
0.295
0.104
0.202
0.297
0.216
0.198
0.124
0.194
Pixel-GS
0.206
0.288
0.111
0.208
0.301
0.188
0.166
0.109
0.167
GDAGS
0.165
0.266
0.094
0.185
0.274
0.092
0.098
0.059
0,070
Table 11: Per-scene memory consumption (MB) from the Mip-NeRF360.
bicycle
flowers
garden
stump
treehill
room
counter
kitchen
bonsai
3
1508
813
1073
1100
1018
397
257
412
259
Pixel-GS
2040
1660
1980
1480
1690
584
592
721
487
AbsGS-0004
1156
751
932
1021
962
234
192
249
234
GDAGS
1030
635
841
683
744
223
146
197
144
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Table 12: Per-scene quantitative results (SSIM) from the Tanks & Temples and Deep Blending.
Truck
Train
Playroom Dr Johnson
Plenoxels
0.774
0.663
0.787
0.802
INGP-Basc
0.779
0,666
0.839
0.754
INGP-Big
0.800
0.689
0.854
0.779
Mip-NeRF360
0.857
0.660
0.901
0,900
3D-GS
0.879
0.802
0.899
0,906
AbsGS-0004
0.886
0,820
0,900
0,907
Pixel-GS
0.886
0.826
0.887
0.905
GDAGS
0.886
0.819
0.901
0.909
Table 13: Per-scene quantitative results (PSNR) from the Tanks Temples and Deep Blending.
Truck
Train
Dr Johnson
Playroom
Plenoxels
23.221
23.221
23.142
22,980
INGP-Base
23.260
20.170
27.750
19,483
ENGP-Big
23.383
20.456
28.257
21.665
Mip-NeRF360
24.912
19.523
29.140
29.657
3D-GS
25.187
21.097
28.766
30.044
AbsGS-0004
25.702
22.010
28,930
29.967
PixeI-GS
25.467
22.028
28.128
29,700
GDAGS
25.617
21.971
29.232
30.166
Table 14: Per-scene quantitative results (LPIPS) from the Tanks & Temples and Deep Blending.
Truck
Train
Dr Johnson
Playroom
Plenoxels
0.335
0.422
0.521
0.499
INGP-Base
0.274
0.386
0.381
0.465
INGP-Big
0,249
0.360
0.352
0.428
Mip-NeRF360
0.159
0.354
0.237
0.252
3D-GS
0.148
0.218
0,244
0.241
AbsGS-0004
0.131
0.193
0.240
0.232
PixeI-GS
0.120
0.178
0.255
0.241
GDAGS
0.136
0.5
0.238
0.233
Table 15: Per-scene memory consumption (MB) from the Tanks & Temples and Deep Blending.
Truck
Train
Dr Johnson
Playroom
3D
530
218
744
504
AbsGS-0004
419
210
457
316
Pixel-GS
1200
904
1280
881
GDAGS
277
175
416
280
19

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Published as a conference paper at ICLR 2026
GT
3DGS
GDAGS
AbsGS
Pixel-GS
Figure 12: Supplementary qualitative analysis results.
20

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Published as a conference paper at ICLR 2026
3DGS
GDAGS
AbsGS
Pixel-GS
Figure 13: Supplementary qualitative analysis results.
21

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Published as a conference paper at ICLR 2026
KROW
12
HERT 100
HES 1300
72-4 KOLLY
F28 Kides
172 1300
TOUS 130M
724 MA00
72 #OM0I
3DGS
GDAGS
AbsGS
Pixel-GS
Figure 14: Comparing over-densification phenomena.
E LIMITATIONS OF GDAGS
While GDAGS demonstrates significant improvements in adaptive densification control and memory efficiency, several limitations remain:
22

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Published as a conference paper at ICLR 2026
Fixed Hyperparameters for Weighting Schemes: The hyperparameter p, which balances performance and efficiency, fixed at 15 by default. While this value works well empirically, its optimal setting may vary across datasets hardware configurations, requiring manual tniing for specific use cases.
Challenges in extreme sparsity: highly sparse regions with minimal gradient activity, GCR may struggle to distinguish between true outliers and inder-rcconstruuted areas, leading to potential under--ensiiocation or over-suppression of Gaussians.
F
LLM USAGE
Large language models (LLMs) were used to assist in the writing and polishing of this manuscript. Specifically, LLM was employed help refine language expression, improve readability, and enhance clarity in certain sections of the paper. Tasks included sentence rephrasing, grammar checking, and improving the overall flow of the text. is important note that the LLM was involved in the conception of ideas, research methodology, experimental design, or data analysis. All scientific content, conceptual development, and analytical results were generated solely by the authors. The use of the LLM was strictly limited to improving the linguistic quality of the manuscript and did extend any scientific reasoning or interpretation. The authors take full responsibility for the entire content of the manuscript, including any text modified or suggested by the LLM. We have carefully reviewed all Al-assisted content to ensure adheres ethical guidelines and accurately reflects the original intent and rigor of our research.
23


Reviews:
[{"review_id": "TKWp9zZ1DO", "reviewer": "Reviewer_QXqY", "summary": "This work trying to achieve a balance between over-reconstruction and over-densification which raise from gradient based adaptive control in classical 3DGS. The authors propose Gradient-Direction-Aware Gaussian Splatting (GDAGS), which introduces the Gradient Coherence Ratio (GCR) to quantify the directional consistency of view-space gradients and a nonlinear dynamic weighting mechanism that regulates Gaussian splitting and cloning based on gradient alignment.", "questions": "1.\tWhy is the directionality measurement of GCR superior to the Pixel weighting mechanism of Pixel-GS?\n2.\tThe nonlinear dynamic weighting model proposed in this paper is intuitive and effective. But why adopt the current form instead of the exponential weighting function among numerous nonlinear models? This point requires sufficient explanation and clarification.\n3.\tThe comparison methods cited in the main text are up to conference works from 2024.  Since over-reconstruction and over-densification are widely discussed topics in the 3DGS community, have there been related works published in 2025?  If so, what are the advantages of this paper compared to those?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe proposed method achieves a good balance between performance and storage, aligns well with intuition, and enhances the overall usability of Gaussian Splatting models.\n2.\tThe 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.\n3.\tThe motivation of this paper is well articulated, and the proposed solution is intuitive. In particular, Figure 1 clearly illustrates the problems of over-reconstruction and over-densification that occur in 3D reconstruction under two extreme scenarios.", "weaknesses": "1.\tThe 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.\n2.\tStill 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 variations should be presented. \n3.\tAlthough experiments show that GDAGS is superior to AbsGS and Pixel-GS but the analysis of the reasons for their performance differences in the paper is relatively insufficient. \n4.\tWhile the GCR is well-defined, the paper lacks theoretical insights into how it affects optimization dynamics or convergence. The justification for its effectiveness is mostly empirical.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hzQeO6BeNo", "reviewer": "Reviewer_dpMa", "summary": "3DGS introduced an adaptive density control algorithm to grow the number of Gaussians to fit the underlying scene structure. However, it is relatively suboptimal in properly splitting and cloning Gaussians, an issue that even prior work such as AbsGS could not fully address. This paper identifies that this issue arises from: 1) gradient cancellation due to diverging sub-gradient directions, and 2) exaggerated gradients caused by the simple aggregation of absolute values of diverging sub-gradients in local regions. To capture the directional consistency of sub-gradients, the authors define the Gradient Coherence Ratio (GCR; Equation 5) and modulate gradients using a nonlinear weighting function defined in Equation 6. Using this criterion, they not only promote the splitting of large Gaussians with diverging sub-gradients but also suppress the cloning of small Gaussians with diverging sub-gradients. Experimental results demonstrate state-of-the-art scene reconstruction quality while maintaining lower memory consumption on standard benchmarking datasets.", "questions": "* Please provide the training duration in the experiments. This will help readers understand the training efficiency of the method.\n* In Figure 3, some images show that GDAGS fails to reconstruct particular areas compared to vanilla 3DGS, which weakens the authors’ argument that GDAGS avoids over-densification and over-reconstruction issues. Do the authors have an explanation for why GDAGS renders these artifacts? For example, GDAGS produces noisy artifacts on the crown molding (top-left of the first-row image) and blobby artifacts in the area between trees and the sky (top-left of the second-row image).\n* One major limitation is that the newly introduced hyperparameters $\\alpha, \\beta, p$ are tuned specifically for different scenes. They need to be searched heuristically to find the best trade-off between quality and efficiency (VRAM usage), which is cumbersome. Is there a learnable approach for these parameters?\n* In Figure 5, the authors show the dynamics of the number of Gaussians associated with clone/split operations. According to the graphs, AbsGS tends to split Gaussians far more frequently than GDAGS, yet AbsGS retains poorer reconstruction quality than GDAGS according to Table 1. This seems counter-intuitive because generally, propagating more Gaussians should improve the reconstruction of complex structures. What is the authors’ explanation for why AbsGS has lower PSNR despite splitting Gaussians much more than GDAGS? In other words, what is the key factor that allows GDAGS to achieve the highest rendering performance without splitting as many Gaussians?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "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.", "weaknesses": "Please see Questions section for my major concerns.\n\nPresentation issues:\n- Math error in Equation 3. The expansion of $T_k$ is incorrect. It must be $\\prod_{j=1}^{k-1} (1-\\alpha_jG'_j(\\textbf{x}'))$.\n- Typo in line 273: $(1 − C_i)^a$ → $(1 − C_i)^p$.\n- Clarify the unit of x-axis in Figure 5. It seems k (thousand).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TxVk7O3lwk", "reviewer": "Reviewer_5e9Z", "summary": "This paper proposes GDAGS, a framework that addresses the over-densification, over-reconstruction, and memory inefficiency issues caused by the ill-posed densification mechanism in the original 3DGS, leveraging the directional consistency method and online dynamic weighting schemes. Experimental results show the efficacy of the proposed method.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well writen and easy to follow.\n2. This paper targets a general and key component in the 3DGS pipeline and provides a simple yet effective solution.\n3. The experimental results show promising performance improvements.", "weaknesses": "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.\n\n2. 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "98G1AI1JPL", "reviewer": "Reviewer_52Pb", "summary": "This paper addresses a fundamental flaw in the \"densification\" process of 3D Gaussian Splatting (3DGS)—the mechanism that decides where to add more detail. The original 3DGS method relies solely on the magnitude (or norm) of the position gradient (which can be seen as the \"rendering error signal\") while completely ignoring its direction. To resolve this fundamental issue, the paper proposes a Gradient-Direction-Aware density control framework (GDAGS). Its core idea is to quantify and leverage gradient directional information to make smarter decisions. The method consists of two main components: The Gradient Coherence Ratio (GCR) and The Nonlinear Dynamic Weighting System. The method not only resolves the blurring issue and improves rendering quality but, more importantly, it reduces redundancy, producing compact 3D models than AbsGS.", "questions": "1. To better understand the method's limitations, the authors should include a targeted experiment on a scene with known sparse viewpoints or textureless surfaces. A qualitative analysis showing how GDAGS behaves in these low-information areas compared to the baseline would be very instructive. Does it fail gracefully by simply not adding primitives, or does it make poor decisions? A discussion of potential fallback strategies in such cases would also strengthen the work.\n2. The authors should report end-to-end training times and compare them against the key baselines. This is essential for a fair assessment of the trade-offs. It would also be helpful to include a brief profiling analysis that quantifies the specific overhead of the GCR computation step.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well-written and easy to follow. \n2. The work's originality lies in its more complete diagnosis of a known issue. While prior work like AbsGS had already identified the problem of gradient cancellation causing blur, this paper correctly points out that this is only half of the story; gradient amplification in aligned regions is an equally important problem that leads to model bloat. The proposed Gradient Coherence Ratio (GCR) is a novel and intuitive metric to directly measure this directional consistency. Using this metric to create a dual-purpose control system—one that simultaneously encourages splits in chaotic regions and suppresses them in stable ones—is a clever and well-motivated approach.", "weaknesses": "1. The authors correctly identify in their limitations section that the GCR metric may be unreliable in very sparse regions with little gradient information. However, this critical failure mode is only mentioned briefly and not explored empirically.\n2. The method introduces new hyperparameters for its weighting function. Although a sensitivity analysis is provided in the appendix, the paper offers little intuition or practical guidance on how these should be set. This lack of guidance could make the method difficult for others to apply effectively to new and different scenes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)