D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction
Authors: Meixi Song
Xin Lin
Dizhe Zhang
Haodong Li
Xiangtai Li
Bo Du
Lu Qi
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian Splatting
Sparse View
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in 3D Gaussian Splatting (3DGS) enable real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations. However, performance degradation and instability remain significant under sparse-view conditions. In this work, we identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage. To address these challenges, we propose a unified framework \modelname{}, comprising two key components: a Depth-and-Density Guided Dropout strategy that suppresses overfitting by adaptively masking redundant Gaussians based on density and depth, and a Distance-Aware Fidelity Enhancement module that improves reconstruction quality in under-fitted far-field areas through targeted supervision. Moreover, we introduce a new evaluation metric to quantify the stability of learned Gaussian distributions, providing insights into the robustness of the sparse-view 3DGS. Extensive experiments on multiple datasets demonstrate that our method significantly improves both visual quality and robustness under sparse view conditions. The source code and trained models will be made publicly available.

D'GS: DEPTH-AND-DENSITY GUIDED GAUSSIAN SPLATTING FOR STABLE AND ACCURATE SPARSE- VIEW RECONSTRUCTION
Meixi S2 Xin Lin Dizhe Zhang? 11 Haodong Li
Xiangtai Li+
Bo Du
Lu Q
Tsinghua University
2 Insta360 Research 3 University of California, San Diego "Wuhan University
Nanyang
Technological University
Dense Views (55 views)
Sparse views (3 views) Ours
Sparse views (3 views) Droppaussian
Gaussian Primitives
Image
Figure 1: Comparison of Gaussian primitives and rendered images between dense views (55 views) and sparse views (3 views) settings. Overfitting occurs in the near field (green box), while underfit- ting appears in the far field (red box). The number of Gaussian primitives the corresponding field shown below the images.
ABSTRACT
Recent advances 3D Gaussian Splatting (3DGS) enable real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations. However, perfor- mance degradation and instability remain significant under sparse-view condi- tions. In this work, identify two key failure modes under sparse-view con- ditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage. To address these challenges, we propose a unified framework D?GS, comprising two key components: a Septh-and-Denssee fitting by adaptively masking redundant Gaussians based on density and depth, and Aestance-Award Fidelity Enhancement module that improves reconstrue- tion quality in under-fitted far-field areas through targeted supervision. Moreover, we introduce a new evaluation metric to quantify the stability of learned Gaus- sian distributions, providing insights into the robustness of the sparse-view 3DGS. Extensive experiments on multiple datasets demonstrate that our method signifi- cantly improves both visual quality and robustness under sparse view conditions. The project page can be found at: website/.
Guided Dropout strategy that suppresses over-
Equal contribution
Project lead
f Corresponding author

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INTRODUCTION
Recently, novel view synthesis (NVS) (Kerbl al., 2023; Lin et al., 2025; Yu et al., 2024; Ye al., 2024; Lee et al., 2024; Niedermayr et al., 2024; Zhang al.. 2024b; Zhou et al., 2024) and its applications have witnessed significant progress due to advances in 3D Gaussian splatting (3DGS), which provides favorable trade-off between reconstruction quality and computational efficiency. While previous methods perform well under Sensely-sampled multi-view settings, acquiring such data in real-world scenarios is often impractical. This limitation has led to growing interest in the sparse-view reconstruction task (Wang et al., 2023a; Yang et al., 2023; Truong et al., 2023; Zhang al., 2024a; Bao et al., 2025), where only few input views are available, posing additional challenges for accurate and consistent novel view synthesis.
To address this challenge, existing works (Park et al., 2025) suggest that 3DGS models trained on sparse views tend to overfit a limited set of Gaussian primitives. Therefore, they typically adopt dropout strategy during training, uniformly dropping Gaussian primitives to reduce over- reconstruction. However, we observe that uniform dropout can inadvertently hurt both well-fitted and under-fitted regions, thereby degrading reconstruction quality critical areas, as shown in the bottom-right of Figure 1. Moreover, the visualizations of Gaussian distributions in dense- and sparse-view settings (55 and 3 input images) reveal two key issues: overrecconstruction in texture- rich and near-camera regions, leading dense, aliased Gaussians and rendering artifacts; under- reconstruction in distant areas, where sparse Gaussians fail to capture structural details, resulting in blurry reconstructions.
Based on these observations, we shift our focus to enhancing and evaluating the robustness of 3DGS models under sparse- view settings through both methodological design and evaluation metric. The proposed D$GS method aims to dynamically adjust the degree of reconstruction based on depth and density information, while the evaluation metric is used to assess the robustness of 3DGS models in consistent training setting. Specifically, the DSGS mainly consists of two key components: a Depth- and-Densite Density guided Dropout (DD-Drop) mechanism and Distance-Awart Fidelity Enhancement (DAFE), to improve the stability and spatial completeness of scene reconstruction under sparse- view settings. DD-Drop assigns each Gaussian dropout score based on local density and camera distance, indicating regions prone to overfitting. High-scoring Gaussians would be dropped with a higher probability to suppress aliasing and improve rendering fidelity. In addition, DAFE avoids underfitting by boosting supervision in distant regions using depth priors.
To further assess the robustness of 3DGS models under sparse-view constraints, we propose a novel evaluation metric, Inter-Model Robustness (IMR), which measures the stability of the learned 3D Gaussian distributions. Specifically, IMR quantifies the consistency of independently trained mod- els by comparing their output Gaussian distributions under identical input settings, reflecting bustmess to initialization and training noise. This distribution- based metric complements traditional image-space metries such as PSNR and SSIM, providing more direct evaluation of 3D represen- tation quality. We comprehensively evaluate the proposed D$GS framework on the LLFF and Mip- NeRF360 datasets. Extensive ablation studies further validate the effectiveness of each proposed module. In summary, the main contributions can be summarized as follows:
We systematically analyze the failure modes of 3DGS in sparse-view settings, revealing consistent patterns of overfitting and underfitting in Gaussian primitives.
Based on these insights, we propose a unified D*GS framework that incorporates two comple- mentary modules: a Depth-and-Denstlee redundant and dense regions, and a Distance-Aware Fidelity Enhancement module enhance reconstruction fidelity in underfitting areas.
Guided Dropout mechanism to suppress overfitting in
To better evaluate the quality of 3D Gaussian representations, we introduce a Gaussian- distribution-basel metric to assess robustness and fidelity beyond conventional 2D evaluations. Extensive experiments demonstrate that D2GS achieves state-of-the-ee novel view synthesis while yielding more robust 3D reconstructions.
2 RELATED WORK
Novel View Synthesis. Novel View Synthesis (NVS) aims to generate unseen views of a scene from given images. Neural Radiance Fields (NeRF) (Mildenhall et al., 2021) reconstruct scenes as
2

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The overall framework of D2GS consists of Depth-and menn-ttsttty Guided Dropout (DD- Drop) module and Distance-Awar Fidelity Enhancement (DAFE) module. The DD-Drop module adaptively removes Gaussian primitives based depth and density indication through dual local- global mechanism. The DAFE module enhances supervision for far-field regions using distance- aware masks.
Figure 2:
implicit volumetric radiance fields, with many works improving rendering quality (Barron et al., 2021: 2022; Verbin et al., 2022: Chen et al., 2022; Barron et al., 2023) and efficiency (Garbin et al., 2021; Yu et al., 2021; Fridovich-Keil et al., 2022; Müller al., 2022; Sun al., 2022; et al., 2023; Wang et al., 2023b; Hu et al., 2023). Despite impressive visual fidelity, NeRF-based methods suffer from high computational costs and long training times. To address these limitations, 3D Gaussian Splatting (3DGS) represents scenes with Gaussian primitives and renders via differentiable splatting, achieving real-time synthesis. Building on this, recent methods further enhance reconstruction in diverse 3D vision tasks (Yu et al., 2024; Ye al., 2024; Lee al., 2024; Kheradmand et al., 2024; Shi et al., 2025; Niedermayr et al., 2024; Zhang et al., 2024b; Yue et al., 2025).
Novel View Synthesis with Sparse Views. NeRF- and 3DGS-based methods have achieved markable performance with dense views, but collecting many images often impractical in real- world scenarios, resulting in significant performance degradation for conventional approaches. To mitigate this, previous NeRF variants introduce architectural enhancements such as semantic con- sistency (Jain al., 2021; Qi et al., 2022; 2023), depth supervision (Deng al., 2022: Niemeyer et al., 2022; Yang et al., 2025; Roessle et al., 2022: Qi et al., 2024; Wang et al., 2023a), frequency regularization (Yang et al., 2023), and cross- view consistency (Truong et al., 2023; Qi al., 2021). With more efficient 3DGS frameworks, recent methods improve scene understanding via pseudo- view generation (Zhang et al., 2024a), address sparse initialization with additional priors (Bao al., 2025), and mitigate overfitting to training views (Park et al., 2025; Chen al., 2025). Recently, some foed-forward methods further advance sparse-view NVS: PixelSplat (Charatan et al., 2024) predicts 3D Gaussian parameters directly from images, MVSplat (Chen et al., 2024) incorporates multi-view stereo cues to improve depth reliability under sparse inputs, and HiSplat (Tang et al., 2024) adopts a hierarchical Gaussian representation enhance geometric detail and view consistency.
PROPOSED METHOD
Figure presents the overall pipeline of the proposed D²GS, which takes sparse-view images as input and generates initial point clouds and camera poses through Fimeture-from-Motie (SfM).
3

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During training, two key modules are introduced: Depth-and-Denat Guided Dropout, which reg- ularizes near-field Gaussians via depth- and density-aware dropout; and Distance-Awan Fidelity Enhancement, which strengthens far-field supervision using depth-derived masks predicted by monocular depth estimator. In the following subsections, we detail the motivation, design, and function of each component, and introduce dedicated robustness metric for 3DGS under sparse supervision.
3.1 MOTIVATION
Our motivation arises from comprehensive analysis of key factors affecting the performance and stability of sparse-view 3D Gaussian Splatting (3DGS). Figure 1 compares the trained Gaussian primitives under dense- and sparse-view settings. It reveals a significant spatial imbalance: Gaus- sians are over- and under-fitted in near-and far-field regions.
Specifically, near-field regions, models trained with only three views (e.g., DropGaussian) pro- duce much higher density of Gaussians than the dense-view model. In the green box, previ- ous methods generated 11,450 Gaussian primitives, far exceeding the 6,112 Gaussian primitives of the dense view, indicating clear local overfitting. After rendering, we observe that local over- reconstruction in near field can introduce artifacts that propagate globally, which significantly degrade the rendered image quality. In contrast, far-field regions suffer from underfitting due to limited visibility in training data and frequent occlusion by densely populated near-field Gaussians. In the red box, previous methods generated 3,082 Gaussian primitives, which is noticeably fewer than the 5,224 Gaussian primitives of the dense view, preventing the optimizer from effectively su- pervising these regions. Therefore, the model is unable to capture accurate geometry and texture in distant areas, leading to blurred or discontinuous structures in the rendered outputs.
3.2 DEPTH-AND--ENNS GUIDED DROPOUT
As observed, near-field regions with high Gaussian density more susceptible overfitting. To alleviate this, we propose a spatially adaptive dropout strategy guided by both depth and density. Furthermore, tackle the problem from both continuous and discrete perspectives, we incorporate two complementary penalty mechanisms operating from local and global viewpoints.
We first introduce the local dropout mechanism, which evaluates the spatial variation of each Gaus- sian primitive i = 1,2,... .... N based on its depth de (Euclidean distance to the camera) and local density P. (estimated via k-nearest neighbors). Both d, and Pt are processed with min-max normal- ization to obtain the depth score d, and density score pr, respectively. The dropout score S is then computed as a weighted combination of the two:
(1) where Wdepth and Wdensity weighting coefficients that satisfy Wdepph Wdensity 1. This con- tinuous scoring function captures fine-grained local spatial variation, but local information alone is insufficient to characterize overfitting patterns across the entire scene.
S = Waepth d4 + Wieessit Pi,
The global mechanism is motivated by depth-induced imbalance: regions at different depth ranges receive markedly different visibility, leading significantly different overfitting behaviors global level. To model this pattern, we further divide the point cloud into three depth- based lay- ers: near, middle, and far. The division determined by the first and second tertiles of the depth distribution, denoted as thresholds Duear and Dmiddle- Here, our method aims to introduce depth prior information without strongly relying on such partitioning. Each layer is assigned a different attenuation factor, where Amiddte and Aiar satisfy 0 < > < Amiddle < 1, and the near layer uses no attenuation.
This combination of locally continuous and globally discrete mechanisms facilitates fine-grained local tuning while preserving global structural coherence, ultimately leading to efficient control over the overall spatial distribution. This combined design controls the probability of per-Gaussian dropout a soft and progressive manner, and the corresponding formulation is given by:
Si,
de S Daear:
P
Amiddle Sr. Afar Ss,
D Daear < d, S D Dmiddles
(2)
dg Dmiddle:
4

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where P. indicates dropout rate of ht Gaussian primitive. Based experimental experience, set Aar = 0.3 and Amiddle 0.7 in practice.
As the training progresses, the number of Gaussian primitives increases through continuous opti- mization and refinement. To maintain effective regularization, we gradually increase the dropout ratio over training iterations using a time-dependen global rate r(t), which progressively increases the fraction of Gaussians discarded in later training stages:
r(t) = mmin + (Tmax - Tmin) min T)
(3)
where í denotes the current training step, fmax and riin are the minimum and maximum dropout rates, and T is the total number of training steps.
3.3 AISTANCE-AWAK FIDELITY ENHANCEMENT
To address underfitting distant regions with missing details, we introduce Distance-Aware Fi- delity Enhancement (DAFE) module that reinforces dedicated supervision in these areas. Specif- ically, we first employ a monocular depth estimation model to generate depth maps for each in- put image. These maps are then processed using depth-thresholding strategy to construct a bi- nary mask that separates the image into near and far regions. The binary distant-region mask Mais {0, 13 #x is constructed as follows:
11 if D(r, y) > T D Dmax:
Mai (I, y) = 00 otherwise,
(4)
where ((x, y) is the estimated depth value at pixel (x, y). Dmaw is the maximum depth value, and T is the predefined depth threshold.
We then leverage the distant-region mask Maia(r, v) to modulate the training objective, with the aim of amplifying the supervision signal in under-fitted far-field regions. Specifically, the mask is applied to both the ground-truth image and the rendered output to isolate distant content. A dedicated distance-enhancee loss is computed by measuring their difference in these masked regions:
DAAFE
Mais Σ Mas(a, (z) v)
(| [[z v) w)|,,
(5)
where i and I denote the rendered and ground-truth images respectively. By incorporating LDAFE, the model is guided to allocate greater attention to distant regions during training, which in turn en- courages the generation of denser set Gaussian primitives in these areas. The improved coverage of Gaussians facilitates more accurate reconstruction of fine-grained details, thereby enhancing the visual quality of novel views in far-field regions.
Following 3D Gaussian splatting, the color reconstruction loss consists of an LI loss and a D-SSIM loss. Accordingly, the overall training objective formulated as:
Livia = (i, + ASSIM LD I) + (DAEE,))
(6)
where ASSIM and ADAFE are weighting coefficients that balance the contributions of the D-SSIM and the DAFE loss.
3.4 INTER-MODEL ROBUSTNESS ASSESSMENT
As shown in Figure (left), repeated training using the same algorithm and configuration can pro- duce results with considerable variance, leading to large discrepancies in rendering quality. This highlights the importance of quantifying the divergence among independently trained models un- der identical settings to assess model robustness. To this end, propose Inter-Model Robustness (IMR), novel metric specifically designed for 3DGS, grounded the theory of 2-Wasserstein Distance (Vaserstein, 1969) and Optimal Transport (OT) (Kantorovich, 1960) over Gaussian point clouds, as illustrated in Figure 3 (right).
Let G1, Gg...., Gn denote independently trained 3DGS models, where each model G₃ consists of K2 Gaussian primitives:
G. {mm,j St.j Bh aigs K
5

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Figure 3: Left: The instability phenomenon of the previous method. PSNR fluctuates significantly across different training rounds, and the quality of the rendered images highly inconsistent. Right: Calculation procedure of the IMR. The Gaussian point clouds abstracted Gaussian mixture distributions, and the Wasserstein Distance and Optimal Transport are used.
where majjj R$ the center, Si,5 R& is the scaling factor, qi R is the rotation, oi R the opacity for rendering ,add Sag R² is I-dimensional color feature. Each Gaussian influences a 3D point in 3D space following the 3D Gaussian distribution:
(.)( (2 (2T)
exp( 1 m..)) " mi,)),,
where the covariance matrix Σi. is computed from the scale 8i, and rotation Gogi-
To enable robustness analysis, each model abstracted as Gaussian mixture distribution:
K:
G =
Σ Way N(m,,, Eig), WiJ
Σ
Oiikk
Here, opacity oij
proxy for the importance of each Gaussian in the final rendering. enabling a principled weighting of geometric features during comparison.
For two Gaussian point clouds, it difficult directly pair tens of thousands of Gaussian primitives one by one. Therefore, to quantify the difference between two such Gaussian mixtures, we employ the 2- Wasserstein distance and OT theory to establish a soft matching. For two Gaussian distribu- N (m,, E2), the Wasserstein distance admits a closed-form via Bures metric (Bures, 1969; Dowson Landau, 1982):
tions μl N (m,1 Σn) and
(21, u2) = || - m + tr(2; + Σn - 2
(10)
This expression captures both the positional distance and the shape difference between two ellip- soidal Gaussians. To avoid expensive matrix square roots and improve numerical stability, ap- proximate the Bures shape term via a first-order Taylor expansion, resulting following expression:
( (M1, (2) ||m, moll 2 tr((E, EE) - -2)))
(11)
The detailed mathematical derivation is presented in the Appendix A. Let G₁ and G2 denote two 3DGS models. The corresponding mixture Wasserstein distance is then formulated as an OT prob- lem over the Gaussian components (Rubner et al., 2000):
K
K2
GW] min
Kyg W2
s.t. Σ
78 W1,6
Σ W2j- (12)
This formulation performs soft structure-aware alignment established by the optimal transport plan E xKiiK2, eliminating the need for explicit correspondence. To compute the distance at scale, introduce entropic regularization and solve the relaxed problem using the Sinkhorn algorithm (Sinkhorm & Knopp, 1967; Cuturi, 2013):
MW2 AGw,aGG, G2) = min Σ Cij +
Σ Yi log Tij ij
(13)

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CoR-Go
DopGrasit
Figure 4: Qualitative Comparison on LLFF dataset (Mildenhall al., 2019). Comparisons were conducted with 3DGS, CoR-GS, DropGaussian. Our method effectively avoids the artifacts and maintains accurate reconstructions.
LLFF (3-view 1/8 Resolution)
L.IPF (3-view 1/4 Resolation)
Methods
PSNR(()
SSIM(t)
LPIPS ())
AVGE(()
PSNR(1)
SSIM(t)
LPIPS (1)
AVGE())
NoRF-bosdd
Mip-NeRF (Barron 2021)
16.11
0.460
15.22
0.351
0.540
0.236
DietNeRF (Jain et al., 2021)
14.94
0.370
0.496
0.233
13.86
0.305
0.578
0.271
RegNeRF (Niemeyer
2022)
19.08
0.587
0.336
18.66
0.535
0.411
0.156
FreeNeRF (Yang et al., 2023)
19.63
0.612
0.308
0.128
19.13
0.562
0.384
0.146
SparseNoRF (Wang
2023a)
19.86
0.624
0.328
19.07
0.564
0.392
3DGS (Kerhl et al., 2023)
19.22
0.649
0.229
0.118
16.94
0.488
0.402
0.180
3DGS-bass coS-hased
DNGaussian
2024)
FSGS (Zhu et al., 2014)
20.43
0.682
0,248
0.108
19.71
0.642
0,283
0.122
CoR-GS (Zhang
2024a)
19.96
LoupSparsetS (Bao et al., 2025)
20.85
0.717
0,205
0.096
20.19
0.680
0.274
0.114
DropGaussian (Park et al., 2025)
20.76
20.01
D'GS (Ours)
21.35
0.746
0.179
0.087
20.56
0.695
0.254
0.107
Table 1: Performance comparisons of sparse-view synthesis LLFF dataset. best, and third-best entries are marked in red, orange, and yellow, respectively.
The best, second-
where Ci = W E1.)), N(m2,, Σ2,))) is the cost matrix, and E >> 0 is the regularization strength. With the introduction of entropic regularization to the original discrete optimal transport objective, the mixture Wasserstein distance between 3DGS models admits a unique and well-defined optimal solution (Delon Desolneux, 2020).
Direct computation of transport between tens of thousands of Gaussians is computationally infea- sible. To further improve tractability, we adopt a depth-stratified importance sampling strategy to select approximately 10,000 Gaussians primitives. Given that far-field Gaussians more prone to noise and instability due to underfitting, they are oversampled accordingly.
Let S MW2 Gy) denote the pairwise distances between N independently trained models. To specifically penalize model pairs with large divergence, we use a weighted formulation that amplifies the impact of inconsistent models. Finally, we define the Inter-model Robustness (IMR) metric as:
IMR Inl Eisicis St, Eistese Sij
(14)

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Methods
PSNR(1)
S
LPIPS(()
AVGE())
3DGS
18.52
0.523
0.415
0.159
FSGS
18.80
0.531
0.418
0.156
CoR-GS
19.52
0.558
0.418
0.146
DropGaussian
19.74
0.577
0.364
0.136
D'GS (Ours)
20.09
0.587
0.356
0.130
Table 2: Performance comparisons of sparse-view synthesis on MipNeRF360 dataset. The best, second-best, and third-best entries are marked in red, orange, and yellow, respectively.
EXPERIMENTS
We conduct experiments on LLFF (Mildenhall et al., 2019) and Mip-NeRF360 (Barron et al., 2022), following the same data splits and downsampling prior work. Our implementation is built on DropGaussian, with 10k training iterations per dataset. The evaluation process uses PSNR, SSIM, PSNR
LPIPS, and AVGE (the geometric mean of MSE = 10 10 v SSIM, LPIPS), along with our
proposed IMR robustness. All experiments run on a single H20 GPU. More Implementation Details are presented in the Appendix B.
Quantitative evaluation. We compare D2GS with some NeRF-based methods (Mip-NeRF, Diet- NeRF, RegNeRF, FreeNeRF, SparseNeRF) and 3DGS-based methods (3DGS, DNGaussian, FSGS, CoR-GS, SoopSparse DropGaussian) on LLFF and MipNeRF360, As shown in Tables and 2, D*GS consistently achieves the best results. On LLFF (1/8 res.), D surpasses FSGS, CoR-GS, and LoopSparseGS by 0.92/0. 990.5 dB PSNR with notable SSIM/LPIPS/AVGE gains, and outper- forms DropGaussian by 0.59/0.55 dB at 1/8 and 1/4 res. On MipNeRF360, it also improves CoR-GS and DropGaussian by 0.57 dB and 0.35 dB PSNR, respectively, confirming its superior reconstruction quality. These gains largely stem from the proposed DD-Drop and DAFE modules, which jointly suppress overfitting in near-field regions while enhancing distant details. More results are presented the Appendix E.
To assess the robustness of the trained 3D Gaussian primitives, we report the met- ric IMR, measuring the dispersion across independently trained models. The num- ber of Gaussian primitives in the scenes of LLFF ranges from 20k to 310k. Ta- ble 3 shows that our method achieves the lowest IMR in both sparse settings: 3.039 (3-view) and 3.109 (6-view), respectively. This indicates more stable and consistent Gaussian reconstructions across runs.
IMR()
Methods
LLFF (3-view)
LLFF (6-view)
3DGS
3.162
3.234
CoR-GS
3.136
3.270
DropGaussian
3.205
3.143
D*GS (Ours)
3.039
3.109
Table 3: IMR comparison on LLFF Dataset with 3- view and 6-view Settings. All results are tested on ten independent training models.
Qualitative evaluation. Figure 4 shows qualitative results on LLFF, comparing 3DGS, CoR- GS, DropGaussian, D*GS, and GT. As highlighted by the red boxes, D2GS yields sharper details and fewer artifacts, preserving more high-frequency structures than DropGaussian with a random dropout strategy. This visual comparison highlights the superiority of D'GS in reconstructing fine- grained geometry under sparse views. These improvements come mainly from the targeted suppres- sion of redundant Gaussians by DD-Drop module and the enhancement of distant structures.
Ablation study on the proposed components. We conduct ablation experiments to validate the effectiveness of each proposed modules on LLFF, as summarized in Table 4. Starting from the baseline without any proposed component, we progressively add the density score, depth score, and depth-based layering for DD-Drop, each of which steadily improves PSNR, SSIM, LPIPS, and IMR. Finally, incorporating the DAFE module further enhances reconstruction quality, leading to the best overall performance. These results confirm that all components contribute complementary benefits, with the full model achieving the highest visual fidelity.
Ablation study on DD-Drop. The upper left part of Table 5 shows different weights Widepth and Wdeneity to balance the influence of normalized depth and density scores in the dropout process. The best performance is achieved when Wdepth 0.5 and Wdensity = 0.5, suggesting that both depth and density contribute positively, and overly increasing the weight of either factor results in
8

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Density Score
Depth Score
Depth-based Layering DAFE PSNR(1)
SSIM(1)
LPIPS(L)
IMR(
19.22
0.649
0.229
3,162
21.02
0.732
0.191
3.119
20.92
0.728
0.200
3.155
21.10
0.735
0.187
3.111
21.17
0.740
0.181
3.088
21.35
0.746
0.179
3.039
Table 4: Ablation Study on proposed components. The Vindicates adding the module.
Umin
mmax
PSNR(1)
SSIM(1)
LPIPS(()
r (%)
PSNR(1)
SSIM(1)
LPIPS(L)
0.05
0.3
21.16
0.740
0.181
5
21.25
0.744
0,180
0.1
0.3
21.11
0.740
0.181
10
21.26
0.743
0.180
0.05
0.5
21.06
0.738
0.187
15
21.20
0.741
0.181
Wdepth
Wdeneily
PSNR(1)
SSIM(1)
LPIPS(L)
ÀDAFE
PSNR(1)
SSIM(1)
LPIPS(.)
0,2
0,8
21.07
0.737
0.183
0.5
21.27
0.743
0.180
0.5
0.5
21.16
0.740
0.181
1.0
21.30
0.744
0.179
0.8
0.2
21.04
0.734
0.190
1.5
21.25
0.743
0.182
Table 5: Ablation study on different parameters in our model. In DD-Drop module, Tmin and m'ax denote the minimum and maximum dropout rates, while wdepth and wdeesiity are used computing the dropout score. In DAFE module, denotes the depth threshold controlling the proportion of far regions retained, and ADAFE denotes the weight of the DAFE loss.
a performance drop. The lower left part of Table 5 presents results under different combinations of minimum and maximum dropout thresholds Tmin and mmax, which control the dynamic range of the dimeneepnnent dropout rate r(t). We observe that setting Tmin = 0.05 and Tmax = 0.3 achieves the best performance, indicating that maintaining mild dropout rate in the early training stages helps to preserve the geometry of the essential scene, while gradually increasing the dropout rate to moderate level encourages effective regularization during the later stages.
Ablation study on DAFE. We further conduct ablations on the components of the proposed DAFE loss. As shown in the upper right part of Table
compare different values for the depth-based masking ratio, where selecting the top 5% of the farthest depth values yields the best performance, indicating that enforcing depth fidelity in distant regions is particularly beneficial under sparse-view settings. In the lower right part of Table 5 investigates the impact of the weighting hyperparameter in the DAFE loss. moderate value (e.g., 1.0) provides the best trade-off across all metrics.
Table 6 compares different depth esti- mation models on DAFE supervision. DepthAnything V2 is used by default. While different depth estimator has an method
Methods
PSNR(1)
SSIM(1)
LPIPS())
MiDaS DPT DepthAnything V2
21.21
0.740
0.182
21.27
0.743
0.181
impact the performance, demonstrates consistent improvements across all models, indicating that DAFE is compatible with a variety of depth priors and effectively enhance rendering quality under sparse-view settings.
21.35
0.746
0.179
Table 6: Ablation Study on different monocular depth estimators: MiDas (Ranftl et al., 2022) with VIT-small backbone, DPT (Ranftl al., 2021) with VIT-Hybrid backbone, and DepthAnything V2 (Yang et al., 2024).
CONCLUSION
In this work, we present novel D2GS for enhancing sparse-view 3D reconstruction. We introduce a depth-and-densiity guided dropout that selectively removes over-fitted Gaussians in texture-dense, Fidelity Enhancement loss
near-camera regions. To complement this, the proposed Distance- leverages depth priors to reinforce geometric consistency particularly in distant regions prone to also assess robustness with an inter-model robustness metric,
underfitting. Beyond accuracy, showing more stable Gaussian distributions across runs. Extensive experiments on standard bench- marks confirm consistent gains in both quantitative metrics and visual fidelity strong baselines.

---

Acknowledgements. This work has been supported by the New Cornerstone Science Foundation through the XPLORER PRIZE.
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12

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A BURES DISTANCE APPROXIMATION
Let G1, G2,...,,... Gn. denote n independently trained 3DGS models, where each model G; consists of K2 Gaussian primitives:
G. [mmi,) St.f. as.. ft.))
(15)
where majj Rt the center, Si,j R23 is the scaling factor, qi R is the rotation, oij R the opacity for rendering ,and Jig R2 is an T--imennional color feature. Each Gaussian influences a 3D point in 3D space following the 3D Gaussian distribution:
1 exp(- m. "T, ma,)),
(r) (2x) E
exp(
(16)
2
where the covariance matrix Σi is computed from the scale st and rotation Gizz To enable robust- ness analysis, each model is abstracted as a Gaussian mixture distribution:
K,
G = Σ Way Nmm,,, Er,), wi,
(17)
K;
E O
Here, opacity oi, serves as proxy for the importance of each Gaussian in the final rendering, enabling a principled weighting of geometric features during comparison.
To quantify the difference between two such Gaussian mixtures, we employ 2- Wasserstein distance. For two Gaussian distributions μ사 N (m,, Σ and μ22 Nm,,, E2), the Wasserstein distance
admits closed-form via the Bures metrie:
W (M1, u2) = ||m - m2 + tr(2; + Eك - 2 2(
(18)
This expression captures both the positional distance and the shape difference between two ellip- soidal Gaussians.
To avoid the computational cost and potential numerical instability associated with computing ma- trix square roots in the Bures distance, we adopt a first-order Taylor approximation for the shape- related term. Specifically, we focus approximating the trace expression in the closed-form for- mula for the squared 2- Wasserstein distance. Define the shape term as:
(E E2) = E + E2 2/
/ [ 2ッ/3 1/2)
)
(19)
To simplify this expression, we perform a change of basis by whitening Eg, such that the resulting expression is centered around the identity matrix. This transforms the matrix product into form suitable for series expansion:
Σ 1/2 "-" =I
where A Σの 1/2
", 1/2 and A E1 Eg.
(20)
By construction, a symmetric matrix quantifying the normalized deviation between E₁ and Eg, and its Frobenius norm scales with the norm of A: Note that ||||| (I]A]|)).
We now expand the matrix square root using a Taylor series about the identity:
(I+ A) = + _ - +
(21)
Substituting this back, we obtain the following approximation:
b 1/2 /
1/2 = Σ' / (1+
(22)
= Σn + 1 L O(1A))).
where the approximation holds up to third-order terms in | F. We now substitute the expansion into the definition of the sharp term 4b
+ = tr (2i + - 2Σ, - -
(23)
to to A) + 1
13

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Method
Training Time (s)
FSGS
425
CoR-GS
223
DropGaussian
56
D'GS (Ours)
Table 7: Training time comparison on LLFF Dataset (Mildenhall et al., 2019) with 3-view.
Finally, combining the approximated shape term with the mean term from the original 2- Wasserstein distance, we arrive at the following closed-form approximation:
W (21, (2) = ||m - m2 + tu(( -2)
(24)
which avoids expensive matrix square roots while preserving second-order accuracy in A. This ap- proximation significantly improves efficiency in large-scale scenarios with a huge amount of large- scale 3D Gaussian primitives.
B IMPLEMENTATION DETAILS
Following the setup in prior works (Kerbl et al., 2023; Zhu et al., 2024; Park al., 2025), begin our pipeline with unstructured multi-view images, which are calibrated using a Structure- from-Motion (SfM). In particular, employ COLMAP generate initial point cloud via dense stereo matching using the "patch-match-stereoo" algorithm, followed by stereo fusion to produce a unified point cloud. Next, we initialize the spherical harmonic (SH) coefficients to degree The 3D Gaussian positions are initialized using the fused point cloud, and we set the opacity to 0.1. Other attributes, such as scale and rotation are initially set to zero.
During training, we initialize the spherical harmonics (SH) at degree 0 to provide a coarse approxi- mation of scene lighting. The SH degree is then incrementally increased by every 1,000 iterations, up to a maximum of degree 3. This progressive refinement allows the lighting representation to capture more detailed effects as training proceeds. The learning rates for different parameters are set as follows: 0.00016 for Gaussian position, 0.0025 for SH coefficients, 0.05 for opacity, 0.005 for scale, and 0.001 for rotation. To maintain stable opacity evolution, we reset the opacity values of all Gaussians to 0.01 every 3,000 iterations. Depth information is obtained per iteration, while density information is recalculated every 500 iterations, with set to 6 the k-nearest neighbors algorithm. The depth information is defined relative randomly selected training camera, consistent with the original 3DGS training setup. As shown in Table 7, the inclusion of these computations increases the training time, but this increase is relatively small and controllable.
C DISCUSSION OF DROPOUT
In DropGaussian, ablation study compares random dropout with selective dropout strategies (e.g., based on gradient or distance), and concludes that random schemes perform better. However, we consider that this observation is not fundamental limitation of selective dropout itself, but rather a consequence of the hard dropout strategy used in their experiments. Specifically, the selective methods in DropGaussian aggressively remove the "top-k" primitives with the highest scores (e.g., most distant), which introduces several issues:
Persistent suppression of specific regions (Discussed in DropGaussian): Since the same sub- set of Gaussians is repeatedly identified as high-score targets, these regions are systematically removed throughout training, causing spatial bias and under-coverage.
Over-suppression in detail-rich areas: In textured or structurally complex regions, high-density Gaussians naturally accumulate. Hard selection blindly eliminates these, inadvertently erasing important details and shifting the model from overfitting to underfitting.
In contrast, our Depth-and- apppesstd--enssty Guided Dropout (DD-Drop) introduces a soft, probabilistic se- lection mechanism. Instead of deterministically removing the most over-represented Gaussians,
14

---

LLFF (6-view)
MipNeRF360 (24-view)
Methods
PSNR(1)
SSIM(1)
LPIPS (J)
AVGE(L)
PSNR(()
SSIM(1)
LPIPS ())
AVGE())
3DGS
23.80
0.814
0.125
0.061
22.80
0.708
0.276
0.092
FSGS
24.09
0.823
0.145
0.062
23.70
0.745
0.230
0.079
CoR-GS
24.49
0.837
0.115
0.055
23.39
0.727
0.271
0.087
DropGaussian
24.43
0.829
0.127
0.057
23.75
0.756
0.227
0,078
DPGS (Ours)
24.84
0.834
0.122
0.055
24.13
0.763
0.221
0.075
Table 8: Performance comparisons of sparse-view synthesis on LLFF dataset (Mildenhall et al., 2019) with view and MipNeRF360 dataset (Barron et 2022) with 24-view.
3-views
6-views
Methods
PSNR(1)
SSIM(1)
LPIPS (
AVGE(L)
PSNR(f)
SSIM(T)
LPIPS (L)
AVGE())
DietNeRF
11.85
0.633
0.314
0.232
20.63
0.778
0.201
0.094
RegNeRF
18.89
0.745
0.190
0.107
22.20
0.841
0.117
0.066
FreeNeRF
19.92
0.787
0.182
0.095
23.25
0.844
0.131
0.063
3DGS
17.65
0.816
0.146
0.102
20.33
0.776
0.223
0.099
FSGS
17.34
0.818
0.169
0.110
21.55
0.880
0.127
0.068
DNGaussian
18.91
0.790
0.176
0.101
22.10
0.851
0.148
0.071
CoR-GS
19.21
0.853
0.119
0.082
24.51
0.917
0.068
0.041
DropCiausiin
20.29
0.863
0.122
0.075
24.57
0.919
0.072
0.042
DPGS (Ours)
21.25
0.865
0.121
0.069
25.25
0.920
0.071
0.039
Table 9: Performance comparisons sparse-view synthesis on DTU dataset (Jensen et al., 2014) with 3-view and 6-view.
compute depth- and density-based dropout score for each primitive and sample dropout actions according this score distribution. This probabilistic characteristic avoids repeatedly discarding the same Gaussians, encourages diversity in regularization, and better preserves important scene structures. Moreover, our dropout probability is further modulated by a global depth-based layering strategy, which adaptively attenuates dropout strength in middle- and far-field regions to prevent excessive loss sparsely visible geometry.
This design distinction highlights that the key to effective selective dropout lies not just in what sig- nals are used (c.g., depth or distance), but how they are applicd. By combining structured guidance with probabilistic flexibility, DD-Drop avoids the pitfalls of rigid selection and achieves superior performance (as shown Table to 3 in the main paper), reconciling the strengths of selective and random dropout schemes.
D LIMITATIONS
Despite the strong performance of D²GS framework in mitigating overfitting and underfitting under sparse-view settings, there remain promising directions for further improvement. For in- stance, while our Septh-and-Density Guided Dropout effectively regularizes the spatial distribution of Gaussians, it relies on hand-crafe crafted depth thresholds and fixed weight coefficients, which may fully capture complex scene-specific priors. Additionally, our robustness metric IMR focuses on inter-model consistency but does not yet consider perceptual stability under dynamic view syn- thesis. Exploring adaptive dropout schedules, learnable supervision masks, and temporally-aware robustness metrics presents fruitful avenues for extending work.
E QUANTITATIVE EVALUATION
Quantitative evaluation. Due to the unconditional dropout strategy used in DropGaussian, its train- ing exhibits significant instability especially under settings with more input views. As result, we found it difficult to reproduce the results reported in their paper, and thus, report the results obtained from our training. Table reports the quantitative comparisons on the LLFF dataset (6- view) and Mi3NeRF360 dataset (24-view), evaluated using PSNR, SSIM, LPIPS, and AVGE met-
15

---

DOGS
Figure 5: Qualitative Comparison LLFF dataset (Mildenhall et al., 2019) with 6-view. Com- parisons were conducted with 3DGS, CoR-GS, DropGaussian. Our method effectively avoids the artifacts and maintains accurate reconstructions.
rics. Among all methods, our proposed DºGS consistently achieves the best or highly competitive performance across both datasets and all evaluation metries.
On the LLFF dataset, D'GS attains the highest PSNR of 24.84 and the lowest AVGE of 0.055, indi- cating superior image fidelity and robust 3D reconstruction stability. also achieves strong SSIM of 0.834 and a low LPIPS of 0. 12,, reflecting better structural and perceptual consistency compared baselines. Similarly, on the MipNeRF360 dataset, D2GS again outperforms other methods with the best PSNR 13). SSIM (0.763), LPIPS (0.221), and AVGE (0.075). Notably, DropGaussian, while competitive, suffers from performance instability due to its unconditional dropout policy, es- pecially under higher input-view scenarios like 2i-v Therefore, report its results based our own reproducible implementation.
We have additionally evaluated D2GS on the DTU dataset (Jensen al., 2014), which is object- centric and has quite different geometry and depth characteristics compared to LLFF and MipN- eRF360. As shown in Table D2GS consistently outperforms all baseline methods across both 3-view and 6-view settings on all evaluation metrics.
These results demonstrate the effectiveness and robustness of DCGS under sparse-view constraints across diverse scenes and datasets.
Qualitative evaluation. Figure 5 and Figure 6 illustrates qualitative comparisons the LLFF and MipNeRF360 dataset among 3DGS, CoR-GS, DropGaussian, D*GS. Our method consistently delivers strong performance with more input views, highlighting its robust generalization capabil- ity. As emphasized by the red boxes, D2GS demonstrates sharper details and significantly reduced artifacts, retaining more high-frequency structural information.
LLMS IN PAPER WRITING
Large language models (LLMs, e-g., GPT-4, GPT-5) were used only for refining grammar and sen- tence structure, with the sole purpose of enhancing readability, clarity, and fluency. They did not contribute to the research ideas, methods, results, or interpretations. All scientific and technical content of this work was conceived, conducted, and written entirely by human authors.
16

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Figure 6: Qualitative Comparison MipNeRF360 dataset (Barron et al., 2022) with 12-view. Com- parisons were conducted with 3DGS, CoR-GS, DropGaussian. Our method effectively avoids the artifacts and maintains accurate reconstructions.
17


Reviews:
[{"review_id": "iJdCYVCnWc", "reviewer": "Reviewer_TLEw", "summary": "This paper presents D$^2$GS, a depth- and density-guided framework designed to improve the stability and accuracy of sparse-view 3D Gaussian Splatting (3DGS). The authors first identify two characteristic failure modes in sparse-view 3DGS: overfitting in near-field regions and underfitting in far-field regions. To address these issues, the paper introduces two main modules:\n(1) Depth-and-Density Guided Dropout (DD-Drop), which regularizes Gaussian primitives using both local and global depth-density statistics to selectively drop overfitted points, and\n(2) Distance-Aware Fidelity Enhancement (DAFE), which applies stronger supervision to underfitted far-field regions using monocular depth-based masks.\nAdditionally, the authors propose a novel evaluation metric called Inter-Model Robustness (IMR), derived from Wasserstein and Optimal Transport theory, to measure stability across independently trained models. Experiments on LLFF and Mip-NeRF360 datasets demonstrate consistent improvements in PSNR, SSIM, and robustness compared with recent 3DGS-based baselines such as CoR-GS and DropGaussian.", "questions": "See the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well written and clearly organized.\n\n2. The proposed depth- and density-based method is supported by empirical observations, and the overall design is reasonable and novel.\n\n3. The paper also proposes an IMR metric for evaluating the stability of 3DGS training, which provides a valuable new perspective for assessing robustness in sparse-view 3D reconstruction.", "weaknesses": "1. The method relies heavily on manually designed depth thresholds, which may limit its adaptability to scenes with large domain variation. It would be desirable for the authors to include comparisons on datasets with greater domain diversity.\n\n2. Compared to a simpler baseline such as DropGaussian, this method introduces several additional heuristics and tunable parameters, but the performance gains appear relatively limited. Beyond analyzing the Gaussian distribution differences, more mechanistic experiments are needed to explain the underlying reason for the improvement.\n\n3. The observation in Figure 1 regarding the depth distribution of Gaussian primitives is presented as a single example and lacks statistical analysis, making it insufficient to demonstrate the generality of the phenomenon.\n\n4. The method relies on monocular depth estimation to generate masks and compute drop ratios; however, it does not account for potential inconsistencies between monocular depth maps across multiple input views, which could cause conflicting depth values and adversely affect Gaussian dropout and reconstruction quality.\n\n5. The paper lacks discussion on feed-forward sparse-view 3DGS methods[1][2][3]. There is an important branch of literature addressing sparse-view reconstruction with general 3DGS frameworks that has not been discussed, resulting in incomplete coverage in the related works.\n\n[1]pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction.\n\n[2]Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images.\n\n[3]Hisplat: Hierarchical 3d gaussian splatting for generalizable sparse-view reconstruction.", "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": "OppzpohLjK", "reviewer": "Reviewer_6fXt", "summary": "This paper studies the instability of 3D Gaussian Splatting (3DGS) under sparse-view settings. The authors identify two common issues: overfitting in near-camera regions and underfitting in distant areas. To address them, they propose D2GS, which combines a depth-and-density guided dropout (DD-Drop) and a distance-aware fidelity enhancement (DAFE) module. The first adaptively suppresses redundant near-field Gaussians, while the second improves far-field reconstruction using depth-guided supervision. The paper also introduces a new evaluation metric, Inter-Model Robustness (IMR), based on the Wasserstein distance between Gaussian mixtures, to measure training stability. Experiments on LLFF and MipNeRF360 show consistent performance gains over existing NeRF- and 3DGS-based baselines.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper presents a clear and well-supported motivation, combining both visual analysis and quantitative evidence. The proposed method is technically simple yet effective, and the introduction of the IMR metric provides an interesting and complementary perspective on model robustness beyond standard reconstruction metrics.\n\n2. The experimental evaluation is comprehensive, showing consistent quantitative improvements, strong qualitative comparisons, and detailed ablation studies. The additional analysis on hyperparameter sensitivity and the influence of different depth estimation models further reinforces the empirical soundness of the work.", "weaknesses": "1. The interpretability and practical utility of the IMR metric for model selection or training guidance remain limited, as its absolute values are still relatively high and its correlation with visual or perceptual quality is not fully established.\n2. The method appears particularly effective for removing artifacts in unbounded scenes with significant depth variation, but its performance on object-centric datasets such as NeRF-Synthetic or DTU remains unclear.", "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": "hY1UFctfTy", "reviewer": "Reviewer_kaRU", "summary": "This work focuses on the issue of \"overfitting in regions with excessive Gaussian density near the camera\" and \"underfitting in distant areas with insufficient Gaussian coverage\" for the sparse-view 3DGS. To this end, this work proposes the Depth-and-Density Guided Dropout strategy for suppressing the overfitting issue and the Distance-Aware Fidelity Enhancement module for improving the reconstruction quality of far-field areas. Experiments on different datasets show the proposed method achieves improvements compared to several baselines.", "questions": "* Ablations in Table 5 seem to show that the parameters have little influence. Can you explain it in more detail?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* This paper is well-written, and the readers can quickly understand the points this work aims to propose.\n* The proposed method is straight-forward, and sounds make sense.\n* The conducted experiments on different experiments show improvements on different metrics compared to other methods, which show the effectiveness of the proposed method.", "weaknesses": "* More experiments for further improving the quality of this work. Experiments of 9 views on the LLFF dataset, like the experiments of other methods.\n* Typo: \n  * For the LPIPS of Table 1 (LLFF (3-view 1/4 Resolution)), CoR-GS achieves the best LPIPS of 0.250, and is supposed to be marked \"red\".\n  * \"AVGS\" or \"AVGE\" in this paper. Should they be the same? This paper includes 4 AVGE and 6 AVGS. If they mean the same thing, the names of them are supposed to be consistent.\n* The method proposed in this work seems more inclined towards engineering techniques, and its innovation is slightly limited, so I present the rating of Boardline Accept, instead of a higher rating.", "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)