Distractor-free Generalizable 3D Gaussian Splatting
Authors: Yanqi Bao
Jing Liao
Jing Huo
Yang Gao
Venue: ICLR 2026 Poster (2026)
Keywords: Distractor-free
Generalizable 3D Gaussian Splatting
training stability
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present DGGS, a novel framework that addresses the previously unexplored challenge: Distractor-free Generalizable 3D Gaussian. Splatting

Abstract: We present DGGS, a novel framework that addresses the previously unexplored challenge: \textbf{Distractor-free Generalizable 3D Gaussian Splatting} (3DGS). Previous generalizable 3DGS works are often limited to static scenes, struggling to mitigate distractor impacts in training and inference phases, which leads to training instability and inference artifacts. To address this new challenge, we propose a distractor-free generalizable training paradigm and corresponding inference framework, which can be directly integrated into existing Generalizable 3DGS frameworks. Specifically, in our training paradigm, DGGS proposes a feed-forward mask prediction and refinement module based on the 3D consistency of references and semantic prior, effectively eliminating the impact of distractor on training loss. Based on these masks, we combat distractor-induced artifacts and holes at inference time through a novel two-stage inference framework for reference scoring and re-selection, complemented by a distractor pruning mechanism that further removes residual distractor 3DGS-primitive influences. Extensive feed-forward experiments on the real and our synthetic data show DGGS's reconstruction capability when dealing with novel distractor scenes. Moreover, our feed-forward mask prediction even achieves an accuracy superior to scene-specific Distractor-free methods.

Published as a conference paper at ICLR 2026
DISTRACTOR-FREB GENERALIZABLE 3D GAUSSIAN SPLATTING
Yanqi Bao Nanjing University Jiangsu, Nanjing, China yanqibac 1970gmail.com
Jing Liao* City University of Hong Kong
Jing Huo"
Nanjing University Jiangsu, Nanjing, China
Hong Kong, China
jingl laociity..edu edu. hk
huojingên edu. cn.
Yang Gao
Nanjing University
Jiangsu, Nanjing, China e..oyynnu.eduuu........
ABSTRACT
We present DGGS, a novel framework that addresses the previously unexplored challenge: Distractor-free Generalizable 3D Gaussian Splatting (3DGS). Pre- vious generalizable 3DGS works are often limited to static scenes, struggling mitigate distractor impacts in training and inference phases, which leads training instability and inference artifacts. To address this new challenge, we pro- pose distractor-free generalizable training paradigm and corresponding inference framework, which can be directly integrated into existing Generalizable 3DGS frameworks. Specifically, in our training paradigm. DGGS proposes a feed-forwant mask prediction and refinement module based on the 3D consistency of references and semantic prior, effectively eliminating the impact of distractor on training loss. Based on these masks, we combat distractor-induced artifacts and holes at inference time through a novel two-stage inference framework for reference scoring and re- re-selection, complemented by a distractor pruning mechanism that further removes residual distractor IDGS-primitte influences. Extensive feed-forward experiments the real and synthetic data show DGGS's reconstruction capability when dealing with novel distractor scenes. Moreover, our feed forward mask prediction even achieves an accuracy superior scene-specific Distractor-free methods.
INTRODUCTION
The widespread availability of mobile devices presents unprecedented opportunities for 3D recon- struction, fostering demand for feed-forward 3D synthesis capabilities from casually captured images video sequences (referred to references). Recent approaches introduce generalizable 3D rep- resentations to address this challenge, eliminating per-scene optimization requirements, with 3D Gaussian Splatting (3DGS) demonstrating particular promise due its efficiency (Charatan et al.) 2024 Liu et al. 2025 Chen et al. 2024b) Zhang et al. 2024a). In pursuit of scene-agnostic inference Trom references 3DGS, existing methods project reference features onto 3D space to predict 3DGS attributes and simulate the complete pipeline: input references, infer 3DGS, and render novel query views, within each training step. This process utilizes selected reference-query pairs for training and optimizes the model to learn the reference-query 3D consistency through query rendering losses.
While promising, this paradigm faces two major challenges real-world, unconstrained capture scenarios due to the presence of distractor (e.g., transient objects such as vehicles or pedestrians). First, during training, real-world data often contains distractor that disrupt 3D consistency, limiting training confined static scenes. Second, during inference, distractor in the reference images cannot be properly projected into 3D space, resulting in unwanted artifacts in the reconstructed 3D scene.
To tackle these issues, we propose Distractor-F free Generalizable 3D Gaussian Splatting (DGGS), novel framework that enhances training stability when training generalizable 3DGS models under
"Coreesooding author

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Published as a conference paper at ICLR 2026
Fachfrwwud Inference
Unoon Distrodar Sumar Data Will
Didrractoo
Naad Viawswll
Moeds
Deemadtoo
Figure 1: Overview of Our Task. Distractors are unwanted transient objects in static scene recon- struction, such as buses, balloons, or anything. DGGS enables feed-forward 3DGS reconstruction from limited distractor data while inferring corresponding distractor masks without extra supervision.
distractor-das data and mitigates distractor-induced artifacts during the inference process. This frame- work builds on two key components: a Distractor-free Generalizable Training paradigm and Distractor-free Generalizable Inference framework. The core idea behind them lies in the discussion about how to predict distractor masks in a feed-forward manner and use them for training and inference. Unlike existing scene-specific distractor-free masking methods that require sufficient input and iterative optimization (Chen et al., 2024a Ungermann et al., 2024 Sabour et al., 2024), method takes advantage of the inherent 3D consistency across references to infer distractor masks in each training iteration. These masks are then applied to exclude distractor regions from reconstruction loss during training, improving stability, and are further used during inference to prioritize cleaner references and suppress artifacts.
Specifically, in Distractor-free Generalizable Training paradigm, design Reference-based Mask Prediction and a Mask Refinement module for generalizable distractor masking, which is based core observation, re-rendered reference non-distractor areas from 3DGS (inferred by references) are generally accurate and robust. Therefore, given an initialized mask, we use these non-distractor areas reference as guidance filter out misclassifidd distractor regions in query. This process relies on the static geometric multi-view consistency of non-distractor areas. For higher mask accuracy, after decoupling the filtered masks into distractor and disparity error areas, our mask refinement module incorporates pre-trained segmentation results to fill distractor regions and designs a reference-baat auxiliary loss to additionally supervise the unnoticed occluded regions in query view. Based on these masks, we propose two-stage Distractor-free Generalizable Inference framework for mitigating holes and artifacts at inference time. In the first stage, we introduce more candidate references and design Reference Scoring mechanism score candidator based on the predicted distractor masks. These predicted scores guide the references selection with minimal distractors and disparity for fine reconstruction in the second stage. To further mitigate ghosting artifacts from residual distractor in the second stage, we introduce a Distractor Pruning strategy that eliminates -atraccto-associatee 3D gaussian primitives.
Overall, we address a new task of Distractor-free Generalizable 3DGS as in Fig. and this is, to our knowledge, the first work explore this problem. For this objective, we present DGGS, framework designed to alleviate distractor-relan related adverse effects during the training and inference phases of generalizable 3DGS. Extensive feed ner-wfr experiments on real distractor data have shown that our approach successfully enhances training robustness and improves artifacts and holes during inference while expanding cross-scene (outdoor scenes training and indoor scenes inference) generalizability in conventional scenc-specifie distractor-free models (Chen et al., 2024al Sabour et al.) 2023 Ren ctt al., 20244, Sabour et al. 2024). Beyond real data, we also construct some synthetic distractor scenes based on Re10K and ACID datasets for further verification. Furthermore, training paradigm achieves better generalizable distractor masking without any mask supervision, outperforming scene-specific training distractor-frt free works(Chen et al. 2024a).
reference- based
RELATED WORKS
2.1 GENERALIZABLE. 3D RECONSTRUCTION
Contemporary advances in generalizable 3D reconstruction seek to establish agnnsti represen- tations, building upon early explorations in Neural Radiance Fields (Mildenhall et al. [021 Waag iet al., 2021 2022 Liu et al., 2022] Bao et al., 2023a). However, these methods face sigmficantt bottlenecks due their lack of explicit representations and rendering inefficiencies. The advent
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of 3D Gaussian Splatting (Kerbl al. 2023 an explicit representation optimized for efficient rendering, has sparked renewed interest in the field. Existing works involve inferring Gaussian primitive attributes from references directly and rendering in novel views. Analogous to NeRF-based
approaches, 3DGS-related methods emphasize spatial comprehension from references, particularly
focusing depth estimation (Charatan al. 2024 Chen et al.] 2024b; Liu et al. 2025 Zhang ct al. 2024a| Liang et al. 2023). Subsequently, ReconX (Liu et al., 2024) and G3R (Chen et al.
2025 enhance reconstruction quality through the integration of additional video diffusion models
and supplementary sensor inputs. The inherent reliance on high-quality references, however, makes generalizable reconstruction particularly susceptible to distractor, a persistent challenge in real world applications. In this study, we discuss Distractor- frree Generalizable 3DGS, an unexplored topic.
2.2
DISTRACTOR FREEE RECONSTRUCTION
focuses on accurately reconstructing a specific static scene while mitigating the impact of dis- tractor (Ren et al.) 2024) (or transient objects (Sabour et al. 2023)). As pioneering work, NeRF- W MMartin-Brualle et al. 2021) introduces additional embeddings to represent and eliminate transient objects in unstructured photo collections. Following similar setting, subsequent works focus on mitigating the impact of distractor al the image level, which can generally be categorized into knowledge-basee and heuristics- based methods.
Knowledge- methods predict distractor using external knowledge sources. Among them. pre-trained features from ResNet (Zhang et al. 2024b Xu et al. 2024), diffusion models (Sabour et al. 2024), and DINO (Ren et al. 2024, Kulhanek et al. (024)) guide visibility map prediction,
effectively weighting reconstruction loss. Recent works (Chen et al. 2024a Otonari et al. 2024
Nguyen et al. 2024) directly employ state-of-the-ard segmentation models such as SAM (Kirillov ct al. (023) or Entity Segmentation (Qi et al. 2022) to establish clear distractor boundaries. Although
these approaches demonstrate certain improvements MMrtin-Brualla et al., 2021: Chen al. 2022
Lee et al. (023) with additional priors, they struggle to differentiate the distractor from static target scenes (Chen al. 2024a] Otonari et al.] 2024). Heuristics-baser approaches employ handcrafted statistical metrics to distinguish distractor, emphasizing robustness and uncertainty analysis (Sabour al. 2023 Goli et al. 2024] Ungermann al. 2024). These methods exploit the observation that regions containing distractor typically manifest optimization inconsistencies. Therefore, they seek to predict outliers and mitigate their impact in residual losses. Regrettably, these approaches suffer from significant scene-specific data dependencies and frequently confound distractor with inherently challenging reconstruction regions, limiting their effectiveness in generalizable contexts.
Recently, there has been increasing advocacy for integrating the two methods (Otonari et al. 2024
Chen et al. 2024a). Entity-NeRY (Otonari et al., 2024) integrates an existing Entity Segmentation (Qi et al. 2022) and extra entity classifier to determine distractor among entities by analyzing the rank of residual loss. Similarly, NeRF-HuGS (Chen et al. 2024a) integrates pre-defined Colmap and Nerfacto (Taniik al.) 2023) for capturing high and low-frequency features of static targets, while using SAM (Kirillov et al.] 2023) to predict clear distractor masks. However, in our settings, acquiring additional entity classifiers employing pre-defined scene-level knowledge such Colmap and Nerfacto is nearly impossible, and residual loss becomes unreliable compared to single-scene optimization due the absence of iteratively refined representation. Moreover, with limited references in unseen scenes, despite obtaining distractor masks, traditional Distractor- methods struggle to handle occluded regions and artifacts. Therefore, we present a novel Distractor-free Generalizable framework that jointly addresses distractor effects in training and inference phases.
3 PRELIMINARIES
3.1 3D GAUSSIAN SPLATTING
3DGS (Kerbl et al. 2023 Bao et al. 2025) represents 3D scenes by splatting numerous anisotropic gaussian primitives. Each gaussian primitive is characterized by set of attributes A, including position p. opacity a, covariance matrix Σ, and spherical harmonic coefficients for color é. To ensure positive semi-definiteness, the covariance matrix Σ is decomposed into a scaling matrix S and rotation matrix R, such that Σ = RSS R Connequuenly, the color value after splatting on view P is:
CP)
Σ MEM
am
(1 az), 1=1
II
(I)
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Samplng Training
Residual
Robnst Mank 1ig
Reline
Per Beratton Sampling
Queey View
Project
3D Garemian
Tiooupple
Project
Project
Bal3
Fill
Segnentation Model
Fill
FiBled
Dal7 Error Nioe
Vieay
Ref-Images
Infer f Ryy2
Musk Depth
Loes.
Rel Los
Combination
1. Mask
Depth
Loss
Masked RG Query Loss
Some-Images
Ref-llased Maski Prediction (4.1.1)
Mask 2ellnereent/.3..2
Fredforward Infer
Ref
Query
Figure 2: Distractor-free Generalizable Training. Based on the sampled reference-query pairs, DGGS first predicts 3DGS attributes and fundamental robust mask M The Reference- based Mask Prediction module then filters this mask, which is further refined through the Mask Refinement module. The entire process is supervised through masked query loss and auxiliary loss.
where c. and am are derived from the covariance matrix Em of the m-th projected 2D Gaussian, as well as the corresponding spherical harmonic coefficients and opacity, respectively.
3.2 GENERALIZABLE 3DGS
Generalizable 3DGS presents a novel paradigm that directly infers 3DGS G attributes from references, circumventing the computational overhead of scene-specific optimization. During each training iteration, existing works optimize model parameters by randomly sampling paired references {1))=1 and query image Ir as inputs and groundtruth under random sampled scenes. Specifically,
g Decoder (J (Encoder (I)))) (P:))=a)
arg argmin |[T (Pr)
where (P:) and Pr are the camera extrinsics of the reference and query images, and N denotes the number of references. The JF denotes the process of projecting encoded 2D features into 3D space and refining (Chen et al. [024bb, Charatan et al. 2024). The 3D features are then decoded into the corresponding 3DGS attributes and rendered into query views, through which the network can be optimized by the query MSE loss.
3.3 ROBUST MASKS FOR 3D RECONSTRUCTION
Unlike conventional controlled static environments, our in-the-wild scenarios contain not only static elements but also distractor (Sabour et al.) (023), making difficult to maintain 3D geometric consistency and training stability. Building on prior Scenc-specific Distractor- free reconstruction research (Sabour al., 2023 2024 Ren et al., 2024 Chen et al. 2024a), we integrate the mask-based robust optimization loss in our pipeline (i.e. 'MVSplat+ in experiment, where represents different mask prediction methods) that can predict and filter out distractor (outliers) in training process. Hence, Eq. 2 is modified, where O denotes pixel-wise multiplication:
arg min Mob + 0
|r 9 (Pr)l
Here, nob represents the outlier masks on query view, where distractor set to zero, while target static regions are set to Our work introduces simple heuristic method (Sabour et al. 2023) as the foundation. The hyperparameters #1 and P2 are fixed across all scenes.
M 1 {C (I [| 9 (Pr)lla < <P1)) > p2).
where C represents the kernel operator and the Pl well as P2 remain consistent with (Sabour
et al. 2023 Despite various mask refinements in follow-up studies OOtonari et al. 2024] Chen
al. 2024a Sabour et al. 2024), their heavy dependence on loss [ ( leads to extensive misc mmisclassificati assification of difficuli-to-feed fere---------d------------------ sordd--orwrrdinfeeeeee parts as distractor regions, as show in Fig. and Fig. which is addressed in subsequent sections.
METHOD
Given sufficient training reference-query pairs, the presence of distractor in either [1)) or Ir (or both) affects the 3D consistency relied upon by generalizable models. Therefore, we aim to design Distractor-free Generalizable Training paradigm in Sec. 4.1 and a Inference framework in Sec. 4.2
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Robast Mask (M
Ref-based Mask (Mq)
Disparity Mask (Mo)
Reference Images
Query Images
Mask Ref
(MM)
Figure 3: The Mask Evolution in Sec. 4.1, Mg is obtained by filtering M Rob from the references non-distractor regions, which is then filled by decoupling MD and using segmentation results get
final M as Eq. 7 Without references filter, target regions are often misidentified as distractor.
4.1
DISTRACTOR-FREE GENERALIZABLE TRAINING
In this training paradigm, we propose Reference-based Mask Prediction in Sec 4.1.1 and Mask Refinement module in Sec 1.2 to enhance per-iteration distractor mask prediction accuracy and training stability scene-agnostically in generalizable setting, as illustrated in Fig.
4.1.1 REFERENCE-BASHE MASK PREDICTION
This Mask Prediction module aims to enhance the MRa accuracy within each iterative various scene, ensuring that optimization efforts remain focused more non-distractor areas. It is essential for generalization training, since Eq. 4 inevitably misclassifies certain target regions as distractor, particularly those presenting challenges for feed-forward inference, in Fig. B which impedes the model's s comprehension geometric 3D consistency in Fig. 5 Our inspiration stems from an intuitive observation: the 3DGS inferred from references maintains stable and accurate re-rendering results in non-distractor regions under reference views. Specifically, we introduce a mask filter that harnesses non-distractor regions from re-rendered references (i.e. from references infer 3DGS and render back to reference views) to identify and remove falsely labeled distractor regions in M under query view, based on the multi-vier consistency of non-distractor static objects. Given E N where i denotes references and fRtf is a hyperparameter (set to 0.001), discussed in Fig. I0 re-rendered reference non-distractor masks MRes, and corresponding projected query masks Mory, are
MRee. 1 { - (P.) < pREI). MMry. W.+T (MRes, D,, Pr.P., (5)
where U represents the camera intrinsic matrix of image pairs, D; corresponds to the depth maps rendered from P, utilizing a modified rasterization library. WL+T defines the image warping that projects each MRee, from P. to Pr using D, and U.
However, given the inherent noise presence in M Marr:,MMrr exhibits limited precision. To solve this problem, incorporate pre-trained segmentation model for mask filling. while designing multi-mask fusion strategy to counteract induued deviations. Following (Chen et al.) 2024a Otonari al., 2024). incorporate state-of-the-an Entity Segmentation Model (Qi to refine MReF; into M ftef. which has the capability to fill different entities when the predicted distractor-region exceeds the threshold total pixel count for that entity. After substituting MRe with En in Eq.
2022
5 we use an intersection operation to fuse all (N) Morr then filter Mob based
Refi
on it, MM@ ( Mory U.M obtaining the reference-baseee mask Mo. which can filter out misclassified regions in Rob to some extent, as shown in Fig
Here, we employ the intersection as conservative strategy to ensure that the filtered-out regions in MR are non-distractor regions acknowledged by all references and preserve the potential distractor excluded from optimization regions, which is crucial for the training process. However, .Mq still exhibits limitations in accurate distractor masking, due to incorrect D, prediction and view disparities, illustrated in Fig. Consequently, the distractor masks undergo further refinement in Sec. 4.1.2
4.1.2 MASK REFINEMENT
Given MQ. straightforward approach is utilize a pre-trained segmentation model refine noise regions and fill imprecise warping areas, discussed with respect MEnn Rie..- In contrast to references, MMq contains distractor regions and disparity-induced emrors arising from reference-query view variations, simultaneously, the latter being present in the query view but absent in all references and primarily occurring at the query image margins. Thus, before introducing the segmentation model, regions decoupling is essential, where the prediction of the disparity-induen inducedd error mask can follow deterministic approach. Given N one-filled masks MI corresponding to different reference
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Novel Testing Scenes
Fax 3005
D665
Couca
Pruze
Hodkel in Regkes
DGGS
Sample
1-A
Stage
Scare Sorting
Stage (Sec.42.2)
Figure 4: Distractor-free Generalizable Inference Framework. DGGS initially samples adjacent references from the scene-imen scene-images pool and leverages trained DGGS for coarse 3DGS. Based on the Reference Scoring mechanism, masks and quality scores are computed for all pool images. These masks and scores subsequently guide reference selection and Distractor Pruning for fine 3DGS.
views P. warp them to the target view PT in Eq. Then, the warped masks are merged using a union operation to ensure that these regions are absent from all references as Fig
N Mn L Pr, (=1
(6)
Finally, we decouple MD from Mq and recombine them after introducing the segmentation model (Qi et al. 2022) and refining the distractor mask. The final refined mask, termed M in Fig. substitutes Rob Eq. to mitigate distractor effects during training. Furthermore, we observe that if only Mask Refinement is used, without leveraging reference and our observation, the misclassification remains severe in Fig. which verifies the importance of reference filter.
Additionally, in contrast to traditional distractor-fre frameworks, references enable auxiliary su- pervision the query view, providing guidance for occluded areas. Specifically,, we re-warp M to reference views and utilize Mir to determine whether occlusion information is included. Therefore, our auxiliary loss LA can focus on areas occluded from query view but visible from references.
ftef;
1=1
LA
Σ Wr-1(1 - M) @ M
En Ref: O II
9 (P.))
All pre-trained segmentation are pre-computed and cached. The final form Eq. modified to:
min M @ | - 9 (Pr) CA.
(8)
0
4.2 DISTRACTOR-FREE GENERALIZABLE INFERENCE
Despite improvements training robustness and mask prediction, DGGS's inference faces two key limitations: (1) insufficient references compromise reliable reconstruction of occluded and unseen regions; (2) persistent distractor in references inevitably appear artifacts in synthesized novel views for feed-forward inference paradigm (encoder-dee) deccodr)))) in Eq. [ To address these challenges, propose a two-stage Distractor-free Generalizable Inference framework in Fig. [4] The first stage employs a Reference Scoring mechanism to score candidate references from the images pool, facilitating the selected references with minimal distractor and disparity. The second stage uses Distractor Pruning module to suppress the remaining artifacts.
Reference Scoring Mechanism. The objective of the first inference stage select references with minimal distractor and disparity among the predefined scene-images pool, adjacent K views (K N) sampled in the test scenes. Therefore, we propose a Reference Scoring mechanism based on the pre-trained DGGS in Sec. S.c.4.1l Specifically, it first samples N adjacent references from the scene-images pool { for coarse 3DGS prediction. We then designate unselected views from {I) as query for mask prediction {M) K -N while the distractor masks of the chosen reference views are [MEn En L. All image masks in pool are scored by Eq. 9 in which top N images are selected in next stage,
(I,)*= ( K E arg max {S ([M]K-M) N; (ME
N
where S is the pixel-wise summation for each mask. In practice, besides distractor size, the extrinsics of candidate images are also crucial reference scoring factors due disparity. Thanks to the discussion of disparity-inducee enror masks in Sec. 4.1.2, we can directly utilize the count of positive pixels in M the single criterion, which selects references that provide better coverage of query view, as shown

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Published as a conference paper at ICLR 2026
Table Quantitative Experiments for distractor-free Generalizable 3DGS under RobustNeRF. *
denotes pre-trained models, + indicates baseline models augmented with existing masking methods.
Statue (RobustNeRF)
Andraid (RabusNeRFF)
Mean (Five Scenes)
Tran
Methods
PSNRT
SSIM
LPIPS]
PSNR1
SSIM
LPIPS]
PSNR1
SSIMF
LPIPS.
Data
Pixclppaa" (2024 CVPR)
18.65
0.673
0.254
17.98
0.557
0.364
20.30
0.704
0.279
Pre PreiTrain
Mvsplat* (2024 ECCV)
18.88
0.670
0.225
18.24
0.586
0.301
20.03
0.722
0.255
Rc10K
Pixclsplat (2024 CVPR)
15.49
0.378
0.531
16.34
0.331
0.492
16.02
0.422
0.511
Mysplat (2024 ECCV)
15.05
0.412
0.391
16.17
0.509
0.381
15.45
0.515
0.426
+RohustNeRR (2023 CVPR)
16.17
0.463
0.382
16.46
0.470
0.411
17.11
0.534
0.400
Re-in on
+On-the-go (2024 CVPR)
14.73
0.366
0.522
15.05
0.440
0.472
15.44
0.476
0.526
Distractor-
+NeRF (2024 CVPR)
18.21
0.694
0.266
18.33
0.640
0.299
19.18
0.700
0.283
Datasets
+HyybriGS (CVPR 2025)
17.16
0.540
0.369
16.37
0.517
0.375
17.82
0.556
0.388
+SLS (Arxiv 2024)
18.11
0.695
0.270
18.84
0.662
0.282
19.29
0.709
0.286
DGGS- TR (wlo Inference Part)
19.68
0.700
0,238
19.58
0.653
0.286
21,02
0.738
0.242
DGGS (Our)
20.78
0.710
0.233
20.93
0.711
0.236
21.74
0.758
0.237
Table 2: Ablation for DGGS- TR and DGGS.
Table 3: Comparison of Tine-Tuned models.
Meihods
Mean (Five Scenes)
Methods
PSNRt SSIMT LPIPS
Ancdetriomphe
PSNKT
Mountain SSIM
LPIPS]
PSNR*
SSIM
LPIPS
Mvsplat*+FT
23.58
0.841
0.08
17.05
0.622
0.202
Baseline (Mvsplat)
15.45
0.515
0.426
Mysplar* S.F.
27.19
0.916
0,084
22.03
0.698
0.160
+Robust Musk
17.11
0.534
0.400
Mvsplat*+DGGSS
28.61
0.068
0.144
++Ref-hased Mask Prediction
20.35
0.701
0.283
SLsppaat-SSS)+-S -SLS-FT
24.18
0.887
0.095
20.77
0.645
0.189
+++Mask Refinement (DGGS. TR)
21.02
0.738
0.242
SLS(Single Scene Train)
22.53
0.77
0.18
wio Reference Eatily Segmantation
20.79
0.733
0.248
DGGS -DGSSTT
29.04 0.931
0.058
23.85
0.787
0.128
w/a Aux Loss
20.64
0.725
0.253
Table 4: Comparison on Efficiency.
DGGS-TR
21.02
0.738
0.242
+ Reference Scoring mechanism
21.47
0.749
0.242
Methods
Pixclsplat Mesplat
DXX38 (Two Stage Inference)
Distractor Pruning (DGGS)
21.74
0.758
0.237
Rendering Time (s)
0.160
0.084
0.148 pre-segmentation 0.111]
in Fig. 78
g In the second stage, wc employ top-ranked images as references for fine reconstruction,
effectively reselecting the candidate images in the pool without increasing GPU memory. Although this approach successfully handles distractor-heav. references, it comes at the cost of decreased rendering efficiency. Optionally, we mitigate this by halving the image resolution in the first stage.
Distractor Pruning. Although cleaner references are selected, obtaining N distractor-fre images in the wild is virtually impossible. These residual distractor propagate via the gaussian encoding- decoding process in Eq. 2 manifesting phantom splats in rendered query view, as shown in Fig. g Therefore, we propose a Distractor Pruning protocol in the second inference stage, which is readily implementable given the reference distractor masks. Instead of direct masking the references, which affects one-to-one mapping between pixels and gaussian primitives in Eq. [ we selectively prune gaussian primitives within the 3D space by directly removing decoded attributes in distractor regions while preserving the remaining components. However, when references exhibit a large amount of commonly occluded regions, the pruning strategy induces white speckle artifacts. Consequently, based on projected masks, DGGS implements the pruning strategy only in scenarios where it not considered a common occluded region for all references, which is discussed in Sec.
5
EXPERIMENTS
5.1 SYNTHETIC DATASET
To train and evaluate DGGS, beyond the real-world datasets On-the-go (Ren et al. 2024) and RobustNeRF Dataset (Sabour et af. 2023), we construct a synthetic dataset to augment the number of distractor scenes based on Re-10K and ACID, with details provided in Appendix(A
5.2 COMPARATIVE EXPERIMENTS
Benchmark: Our Distractor-free Generalizable training and inference paradigms can be scamlessly integrated with existing generalizable 3DGS frameworks. We adopt Mysplat (Chen et al.) 2024b) as our baseline model. Extensive comparisons are conducted against existing works trained under same settings and distractor datasets, including: (1) retraining existing generalization methods (Chen 2024b Charatan et al.) 2024). and (2) retraining Mvsplat (Chen et al. 2024b) '+' different mask prediction strategies from distractor-fres approaches (Ren et al. 2024 Sabour et al. 2023 Chen et al.) 2222a| Sabour et al., 2024). Although all current distractor free works scenc-specific,
et al.
most of them focus on applying distractor masks in loss function (as discussed in (e2.2..). so we can directly transfer them to the query rendering loss under our baseline. Specifically, during the training
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Pinclegiat
Misplat
On the go
Mvoplat+
Mvsplat+ RobustNeki
Mvsplac+ NeRI-HUGK
Mvsplase
Figure 5: Qualitative Comparison of Re-trained Existing Methods across unscen scenes.
Images
Robust Mask
(Mit
NeRF-HUGS (Semm-spocifle)
(A-)
Ous
Images
Robras Mask
(M
(Scone-speciff))
NeRF-HUGS
(M)
Ours
Figure 6: Qualitative Comparison for our masks prediction vs. scene-specific results.
phase, combine these mask prediction methods from existing works and replace M in Eq Furthermore, (3) we compare with existing pre-trained generalizable models (distractor-frre).
Quantitative and Qualitative Experiments: Tab. 1 Fig- and Fig. qquatitaaivvll and qualitatively compares DGGS-TT TR (only TRaining in Sec. [4.1 without two stages inference Sec. 4.2) and DGGS with existing methods. The results analyzed from three aspects: re-training models, pre-training models and models after fine-tuning.
For Re-train Model: Tab. 1 and ddemonsatttttttee that our training paradigm significantly enhances training robustness, outperforming existing generalizable 3DGS algorithms, PSNR: 21.02 vs. 15.45, under same training conditions. Compared introducing existing scene-specific distractor-free approaches, where overaggressive distractor prediction degrades reconstruction fidelity, DGGS-TR exhibits enhanced reconstruction detail and 3D consistency, PSNR: 21.02 vs. 19.29, through precise distractor prediction, which is further validated in Fig. G Similarly, the qualitative results in Fig. show training instability and significant performance drops, even without distractors in some test references. It confirms that distractor presence severely impairs model learning of geometric 3D consistency, aligning with our motivation. Additionally, experiments also show that DGGS possesses cross-scene feed-forward inference capabilities that existing distractor-fie free methods lack.
For Pre-train Model: Tab. rreport comparisons between DGGS-TR, DGGS and existing pre-train generalizable models. Compared with pre-train models distractor-free data, DGGS-TR exhibits superior performance even with training on limited distractor scenes, mainly due to mitigating the inference scene distractor impacts and domain gap. Fig. 7 also illustrates similar findings: DGGS-TR effectively attenuates partial distractor effects in the 3D inconsistency regions. Furthermore, after introducing our inference paradigm, DGGS demonstrates 'pseudo-completion and artifact removal capabilities, which benefit from the Reference Selection mechanism that can choose references with less distractor as well as disparity and the Distractor Pruning during inference process as Fig, To further validate effectiveness, same data domain as pre-trained MVSplat* except for inserted distractor. In Fig. DGGS shows better resistance distractor, effectively mitigating artifact. More results are shown Appendix.
compare DGGS's 's results on synthetic data, which maintains the
After Fine-tuning: To further demonstrate DGGS's capability, we conduct fine-tuning experiments on the different pre-trained models (including Mvsplat*, (Mvsplat+SLS)> and DGGS-TR* corre- sponding Line 2, 8, and 9 in Tab. and training strategies (including FT, SLS--TT and DGGS- corresponding Mysplat (Chen et al., 2024b). SLS (Sabour al. 2024h and Ours). We fine-tune using the 'clutter' data and evaluate on the "extra" data in test scenes. For fairness in comparison, our inference framework is not employed. In Tab. DGGS-TR demonstrates significant improvements
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(Petrac))
DGGS-TR
DGGS
GT
Mortiar(Pretttiii))
DGGS-TR
DGGS
GT
Figure 7: Qualitative Comparison of Pre-trained Models, DGGS-TR and DGGS.
I Refenence Scuring mechanism
Sampling (DGGS-TR)
-Disracter Pruning (DOGS)
Musplat".
Mysplat
(Pretrain)
DGGS
(Pretiin)
DGGS
Figure Ablation for Inference Strategy.
Figure 9: Comparison synthetic data.
over existing models and strategies, attributed to reliable distractor prediction. Furthermore, we find that DGGS's s single-scene fine-tur tuning performance is even better than SLS's single-scene training (metrics reported by SLS (Sabour et al. 2024 This benefits from DGGS's pretraining large-scale datasets and its reference-based strategy for distractor pridiction.
Efficiency: Tab. 4 shows the efficiency comparison. Due the segmentation model and two-stage inference, DGGS has slight decrease in efficiency compared to (Chen et al.) (024b). but mitigates distractor and obtains masks. It can be boosted by reducing segmentation resolution.
Re-rendered Reference Accuracy: One of the core insights of DGGS is to leverage the more stable reference re-render
additional filter to assist query distractor mask prediction. Most existing works exhibit this trend, such as Mvsplat* (Chen et al. (024b)). To verify the stability of re-rendering during the generalizable 3DGS entire training process, we compare the performance of query and re-rendering reference views in Tab, 6 From the experimental results on DGGS, it can be easily observed that from the beginning of DGGS training, reference re-rendering exhibits more stable performance relative to the query view. This characteristic allows reference re-rendering to effectively guide distractor mask prediction for the query view. Furthermore, we simultaneously observe that as the training process progresses, the performance gap between the two gradually narrows.
5.3
ABLATION STUDIES
5.3.1 ABLATION ON TRAINING FRAMEWORK
The upper section of Tab. 2 and Fig. show the effectiveness each component in DGGS training paradigm. The Reference- based Mask Prediction combined with Mask Refinement successfully miti- gates the tendency of over- predicicig targets as distractor that occurred in the original M Within the Mask Refinement module, Auxiliary Loss demonstrates remarkable performance, while Mask Decoupling and Entity Segmentation contribute substantial improvements to the overall framework. Furthermore, our analysis Fig. reveals that DGGS-TR achieves scene-agnostic mask prediction capabilities, with 2024a). Note that, for fair evaluation, all masks are predicted under DGGS-TR based same references, without the involvement of our inference framework.
inference results superior to single-scene trained models (Chen et al.
5.3.2 ABLATION ON INFERENCE FRAMEWORK
The bottom portion of Tab. 2 Fig. 7 and Fig. analyze the component effectiveness within the inference paradigm. Results indicate that although the Reference Scoring mechanism alleviates the impact of distractor in references by re-selection, red box represent distractor references in Fig. S certain artifacts remain unavoidable. Then, our Distractor Pruning strategy effectively mitigates these residual artifacts. We also analyze how the choice of K and N in DGGS, the sizes of scene images pool and references, affects inference results in Fig. 10 Generally, larger values of K yield better performance up to 2N, beyond which performance plateaus, likely due to increased significant view disparity in the images pool. A similar situation occurs with N, where excessive references actually

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Table Ablation Study on Segmentation Module under RobustNeRF dataset.
Table 6: Query v.s, Re-rendering Reference Analy-
sis comparing PSNR different training iterations.
Method
PSNR1
LPIPS
Mothod
Train Stage
Query
Ref Re-render
DGGS-TI TR (wlo Segmentation)
20.67
0.730
0.251
Trained Mvsplat
Done
26.02
29.71
DGGS-TI TR (SAM2)
21.07
0.740
0.240
DXGGS-TR Segmeentatio)
21.02
0.738
0.242
DGGS-TR
12.13
17.82
DGGS (wle Segmentation)
21.41
0,749
0.240
DGGS-TR
1000
16,36
20.38
DGGS (SAM2)
21.77
0.758
0.236
DGGS-TR
10000
22.11
25.65
DGGS (w/ Segmentation)
21.74
0.758
0.237
Trained DGGS TR
Done
26.51
28.02
increase distractor and disparity. And, Tab. 13 explores why the Reference Scoring mechanism brings performance improvements. potential possibility our ability to access more references during inference (Chen et al. 2024b). Therefore, we directly select more references in DGGS-TT TRR Experimental findings indicate, incorporating additional references directly will introduce novel artifacts. It proves that the quality of references takes precedence over quantity in our setting, and the proposed Reference Scoring mechanism can filter out higher-quality references.
5.3.3 ABLATION ON PRE-TRAINED SEGMENTATION MODEL
Pre-trained entity segmentation has been widely used in previous scene-specific distractor-fres et al. 2024. Chen et al.) 2024a). We further analyze the role of the pretrained segmentation model in the training and inference stages of DGGS in Tab. lan. Tab. [ is not difficult to find that whether during training inference, the pretrained segmentation model provides limited performance improvement compared to other modules, such as reference-based mask prediction. This is attributed to the fact that the core factors for stable and generalizable 3DGS training 3D consistency among reference views, rather than fine- --gainrindd distractor segmentation boundaries. During the inference process, multi-reference inference and the reference scoring mechanism similarly contribute to enhancing the robustness of DGGS with respect to segmentation results. Furthermore, we also replace the Entity Segmentation model with the more robust segmentation model SAM2 (Ravi) et al., 2024). Although it is capable of outputting more accurate distractor segmentation masks, experiments demonstrate that its impact on overall reconstruction performance limited. This also further demonstrates DGGS's s robustness to predefined segmentation results in terms of reconstruction performance. More discussion about the segmentation model is presented in the Appendix, [B.4
(iallessCatly-Chhaae
CONCLUSION
Distractor-free Generalizable 3D Gaussian Splatting presents a practical challenge, offering the potential to mitigate the limitations imposed by distractor scenes on generalizable 3DGS while ad- dressing the scene-specific training constraints of existing distractor free methods. We propose novel training and inference paradigms that alleviate both training instability and inference artifacts from distractor data. Extensive experiments across diverse scenes validate our method's is effectiveness and demonstrate the potential of the reference-based paradigm in handling distractor scenes. We envision this work laying the foundation for futuree community discussions on Distractor-free free Generalizable 3DGS and potentially extending to address real-world 3D data challenges in broader applications.
Limitation: DGGS mitigates distractor but experiences performance degradation under extensive common occlusions. Since DGGS does not have additional generative completion capabilities, will exhibit phenomena such speckles when facing situations with common occlusions among all references, as shown in Fig. [6] Generative models offer potential solution. In addition, the sacrifice in efficiency is unavoidable, which can be mitigated through lighter segmentation models and resolution.
REPRODICIBILITY STATEMENT
We have made every effort to ensure work is reproducible. Our experiments are conducted on the public Mvsplat benchmarks. The methodology for constructing the DGGS is detailed in See To further facilitate replication, provide comprehensive training configurations and implementation details in appendix A The source code for our framework and experiments will be made publicly available upon publication.
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ACKNOWLEFGEMENTS
This work is supported in part by the National Natural Science Foundation of China (62276128, 62192783), Jiangsu Natural Science FounGation(KK3224055)) Jiangsu Science and Technology Major Project (BG2024031), the Fundamental Research Funds for the Central Universities( (1 143801 18,8018,, KG202514), a grant from the NSFC/RGC Collaborative Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and National Natural Science Foundation of China (Project No. CRS_ HKUST605/25), and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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A EXPERIMENTAL DETAILS
A. REAL AND SYNTHETIC DATASET
In accordance with existing generalization frameworks, DGGS is trained in a cross-scene setting with distractor and evaluated in novel unseen distractor scenes to simulate real-world scenarios. Specifically, we utilize two widely used mobile-captured datasets: On-the-go (Ren al., 2024) and RobustNeRF (Sabour et al., 2023), containing multiple distractor-scenee in outdoor and indoor environments, respectively. For fair comparison real data, we train all models on On-the-go outdoor-scenes except Arcdetriomphe and Mountain, which, along with the RobustNeRF indoor- scenes, serve as test scenes. In addition to real data, we also construct synthetic datasets for additional verification.
A.1.1 REAL DATASET
DGGS primarily addresses distractor challenges in both training and inference under generalization settings. To validate the reliability DGGS the wild, we conduct experiments on existing real- world distractor datasets, which combine the On-the-go (Ren al. 2024) (outdoor scenes) and RobustNeRF (Sabour al. 2023) (indoor scenes) datasets. These datasets are captured by mobile devices and constitute the most widely utilized benchmark for scene-specific distractor-fre free methods, as discussed in Sec. 2.2.
It, captured using an iPhone 12, Samsung Galaxy S22, and DJI Mini 3 Pro drone, provides a diverse and dynamic range of distractor with varying occlusion ratios (5% to 30%). The dataset consists of 12 casually captured sequences, including Drone, Patio, and AredeTriomphe, most of which are outdoor scenes.
On-the-go Dataset:
RobustNeRF Dataset:
comprises four natural indoor scenes two an apartment and two in robotics lab-captured with controlled settings and varying complexities, including dynamic distractor and fixed camera parameters.
We downsample all images" resolution to 192 256 in all experiments. As described in Sec. 5.1, we designate all scenes from On-the-go dataset except Arcdetriomphe and Mountain as the training set, while these two scenes along with the RobustNeRF dataset serve evaluation scenes. For evaluating the robustness against distractor, we utilize the 'extra' views from the On-the-go dataset and the 'clean' views from the RobustNeRF dataset (except for the Crab/) inference views, and compute corresponding evaluation metrics to assess performance. Note that all reference-query pair selection strategies ensure distinct poses between them, particularly for scenes with clean-clutter paired configurations.
A.1.2 SYNTHETIC DATASET
To further validate our novel task: Distractor-free Generalizable 3D Gaussian Splatting, we construct numerous synthetic scenes based on Re10K (Zhou et al., 2018) and ACID (Liu al.. 2021) datasets that are widely used in the field of generalizable reconstruction (Chen et al. 2024b). These datasets, sourced from YouTube, contain numerous multi-view images of static indoor and outdoor scenes with corresponding camera poses that we use as the foundation to construct distractor on them. For distractor, we select the COCO (Lin et al. 2014) dataset as our source, contains numerous object categories with their corresponding semantics and masks.
Specifically, we build distractor library using COCO masks and randomly introduce different distractor to random positions with random rotations in the multi-view images of various static scenes. It's worth noting that for multi-view images of same scene, we ensure the inserted distractor the same object, which better aligns with real-world scenarios. Unlike real-world scene data, we can control the frequency of distractor appearances and their coverage area in images, which provides further validation for DGGS. We ensure that 70% of the images contain inserted distractors while the remaining images remain distractor- free. Additionally, through scaling the distractors, we control their occupied area consistently range between 0% and 30% of the entire scene. During evaluation, we maintain consistent settings with the Real Dataset, including the selection of references and queries and the evaluation methodology.
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Ablation studies of
Ablation Studies of Y and
21.75
21.24
21,70
21.8
21,50
$21.6
21.50
21:41
2214
21.25/2114
1.5%
2.5V
3N
N+2
3+4
Awe
Av,00.0001
Av,
Size Images Pool Durtng Inference
Parameter Vallon
Figure 10: Performance Comparison under Different Inputs, and Ablation Studies for PRef*
A.2 TRAINING AND EVALUATION SETTING
In all experiments, we set the number of references N=4 and the size of scene-images pool K==, which are discussed in Fig. TO, During all training, query views and reference views are selected, regardless of 'clutter' or 'extra". In evaluation phase, we utilize all 'extra' images and 'clear' images with stride of eight as query views for On-the-go (Aredetriomphe and Mountain) and all RobustNeRF scenes. For references in evaluation, we construct the scene-images pool using views closest to the query view, ensuring inclusion of both distracttor-comtaiinng and few distractor-free data to validate the effectiveness of Reference Scoring mechanism. Note that this setting is only for validation and evaluation purposes. In practical applications, the scene-images pool can be constructed using any adjacent views, independent of the query view and distractor presence. Finally, compute the average PSNR, SSIM, and LPIPS metrics across all scenes on all query renders. More details are described in Appendix.
A.2.1 EXPERIMENTAL SETTING
To ensure fair comparison, maintain hyperparameters, experimental settings, and evaluation method across all re-training experiments. All re-trained models are implemented with Py 2. 1.0, and all experiments conducted using Nvidia RTX 3090 GPUs with CUDA 12.1 and trained for 10K iterations.
A.2.2 OTHER RE-TRAINED COMPARATIVE METHODS
addition maintaining consistent experimental settings as mentioned above, we conduct com- parative experiments by integrating firtractor-free approaches with generalizable 3DGS methods. Specifically, we incorporate existing distractor-free mask prediction and loss function improvements into baseline model, Mvsplat (Chen al., 2024b). This integration approach is viable primarily because existing distractor- -free free methods largely emphasize residual-basee based mask prediction, facilitat- ing seamless incorporation into the query residual loss framework within generalizable configurations. The integration methodology varies according to different approaches, as detailed below:
RobustNeRF (Sabour al. 2023) and SLS (Sabour et al.) 2024): We directly incorporate RobustNeRF's and SLS's mask prediction component to guide the MSE loss in Mvsplat (Chen et al.) 2024b).
NeRF-On-the-go (Ren et al., 2024): While maintaining all other components unchanged, we similarly incorporate DINOv2 (quub et al. 2023) to predict uncertainty maps for guiding the MSE
loss in Mvsplat (Chen et al. 2024b).
NeRF-HuGS (Chen et al. 2024a): To maintain maximum consistency with the original method, incorporate both SAM Kirillov al. 2023) and predefined Colmap (Schonberger & Frahm 2016) to guide mask prediction in MvSplat (Chen et al. (0245)'s 's MSE loss. We exclude Nerfacto [Tancik al. 2023) from our implementation, the per-scene pretraining becomes impractical under our iterative scene transition setting.
B ADDITIONAL RESULTS AND ANALYSIS
This section presents extensive experimental results, both qualitative and guantitative, across various scenarios, with further analysis validating the efficacy of DGGS.
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Table 7: Quantitative Experiments for DGGS under single-scene setting
Statue (Robus(NeRF)
Methods
PSNR
SSIMI
LPIPS.
NeRF-W Martin Martin-Brualla et
2021) (CVPR 2021)
18,91
0.616
0.369
HA-NeRI (Chen et al. 2022)
(CVR 2022)
18.67
0.616
0.367
RobustNeRI: (Sabour
al.
2023) (CVPR 2023)
20.60
0.758
0.154
On-the go-the-go (Ren et al.
2024
(CVPR 2024)
21.58
0.77
0.24
NeRF-HuGSS (Chen et al.
2024a)
(2024 CVPR)
21.00
0.774
0.135
SLS (Sabour al. 2024)
(Arxiv 2024)
22.69
0.85
0.12
DGGS-TR
21.57
0.769
0.187
DGGS-TR (+ Distractor-free References)
30.50
0.952
0.052
Table 8: Memory Usage and Time under dif- ferent configurations.
Table 9: Ablation Study on Segmentation Model for Mask Prediction.
N (8)
Resolution
Memory
Time (wla Seg)
Method
PSNR1
SSIM
I.PPSS].
4 (8)
252
10G
0.111s
DGGS-TR (Feature Consistency)
20.85
0.733
0.242
4 (8)
504
16G
0.277s
DGGS-TR (Clustering)
20.92
0.736
0.245
(12)
252
12G
0.203s
DGGS-TR (Our)
21.02
0.738
0.242
B. QUANTITATIVE RESULTS IN MORE REAL-SCENER
Qualitative comparisons of DGGS in RobustNeRF scenes are presented in Tab. 1. Furthermore, present results on scenes Arcdetriomphe and Mountain from the On-the-go dataset in Tab. [2] It is worth noting that we omit the discussion of DGGS (with Our Inference) for these scenes quantitatively as their 'extra' views used for evaluation still contain partial distractor. The qualitative comparisons for them are extensively discussed subsequent sections. Extensive quantitative evaluations across diverse scenes validate the superiority of DGGS, notably outperforming existing generalizable methods that suffer from distractor- cearrcctor-nnuced induced uncertainties. Although augmenting the baseline with scene-specific distractor-fres free methods improves stability in some scenes, DGGS's ' scene-agnostic mask prediction capability demonstrates superior performance in generalizable distractor-free 3DGS reconstruction.
B.2 MEMORY USAGE AND TIME ANALYSIS
To further analyze the memory Usage and time for DGGS, we additionally report the efficiency under different N(K) and differrnnt resolutions in Tab. The impact of these factors on efficiency is similar to works like Mvsplat (Chen et al., (024b); that is, as the resolution and N increase, the inference time and memory usage of DGGS also increase accordingly.
B.3 ABLATION STUDY FOR MASK FUSION
DGGS adopts conservative intersection operation reference multi-mask fusion strategy in Sec 4.1.1 which effectively reduces the impact of distractors during generalizable 3DGS training. To further verify the effectiveness of this method, we additionally discuss some variant mask fusion approaches. In TTb we modify the original intersection based mask fusion strategy to require least 50% of the references to agree, meaning that if two references indicate that the corresponding location is static region, it can be considered static. The experimental results show that this leads decline in the training model's ss performance, which may be due to distractor regions being misclassified as static regions. This further demonstrates what we mention earlier: incorrectly classifying dynamic regions static has greater impact generalizable 3DGS training than incorrectly classifying static regions as dynamic. Furthermore, we also discuss utilizing soft weighting as an alternative to the strict intersection of all references during inference. A straightforward approach is employ the view angle as an additional condition for weighted fusion all masks, whereby references that are more distant from the target view assigned proportionally lower weights. Experiments
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Table 10: Quantitative Experiments for DGGS on Synthetic Testing Scenes
Synthetic Testing Scenes (Mean)
Methods
PSNR1
SSIM
LPIPS.
Mvsplat* (Pre-train on Re-10K) (Chen al. 2024b)
18. 18.02
0.758
0.220
DGGS (Real + Synthetic)
26.51
0.912
0.105
DGGS (Fine-tuning on Synthetic Training Scenes)
29.66
0.949
0.059
Table Ablation Study for Mask Fusion.
Method
PSNRT
SSIM
LPIPS|
Method
PSNRT
SSIM
LPIPS].
DGGS-TR (50%)
20.70
0.734
0.250
DGGS (View Angles)
21.60
0.752
0.240
DGGS-TR (Our)
21.02
0.738
0.242
DGGS (Our)
21.74
0.758
0.237
demonstrate that distractor masks are niew-independent, and such fusion leads to additional distractor artifacts some cases. This means that a distractor appears in any reference view, regardless of its distance from the target view, it negatively affects the reconstruction quality of the entire scene.
B.4 ABLATION STUDY FOR SEGMENTATION MODELS
To further discuss the importance of pretrained segmentation models, additionally introduce feature consistency loss terms or Colustering strategies to replace the segmentation model. These strategies are verified to have certain robustness against some misclassified regions for distractor. From the results in Tab. / it can be found that the pretrained segmentation model can be replaced during the training process without causing serious impact on reconstruction performance. This is similar to our previous analysis, that is, DGGS mainly relies on 3D consistency among references to determine the distractor and stabilize the training process, rather than accurate masks. However, the difference from these methods is that they can hardly output accurate distractor masks additionally, whereas DGGS is able to simultaneously achieve distractor mask prediction during the reconstruction.
B.5 QUANTITATIVE RESULTS IN SYNTHETIC-SCENC
Corresponding to Fig. 10 in the main text, we quantitatively discuss the performance comparison of DGGS synthetic scenes. We discuss training settings for DGGS, including training on real scenes and directly inferring scenes using synthetic scenes. To on unseen scenes, the scenes used for fine-tuning are kept inconsistent with the scenes used for inference. Tab. 10 demonstrates the generalization ability of DGGS despite the significant domain gap between the real scenes used training and the synthetic scenes used for testing. Meanwhile, this also shows that the pre-trained Mvsplat* struggles to handle the impact brought by distractors, which may be caused by 3D inconsistencies. We further verify this in subsequent qualitative experiments, shown in Fig.
synthetic scenes, well as fine-tuning the model trained on real
B.6 QUALITATIVE EXPERIMENTS
Fig. 14 and Fig. 15 present comprehensive qualitative comparisons of DGGS-TR and DGGS against existing methods. Through extensive visualization results, DGGS-TR demonstrates superior stability, particularly high-frequency regions, compared to other methods trained on distractor-rich data. However, its performance deteriorates when processing reference images containing substantial distractor. While marginally outperforming pre-trained models (Chen et al. 2024b). it continues to face challenges with artifact suppression and hole elimination. Leveraging DGGS effectively addresses these challenges. Extensive examples in Fig. 15 validate its ability to mitigate artifacts and holes, producing cleaner scene representations.
inference strategy,
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Rafesence Iniages
(Pertii)))
DGGS-TR (Training Keal-data)
TXGGS (Tmining Real-data)
GT (ads Distrcttr))
Figure 11: Qualitative Comparison on Synthetic scenes with Same References.
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Table 12: Quantitative Experiments for distractor-free Generalizable 3DGS under Arcdetriomphe and Mountain scenes.
Arcdetriomphe SSIM
Mountain SSIM
Methods
PSNRT
LPIPS|
PSNR
LPIPS
Pixelsplat (Charatan et al. 2024)
15.15
0.311
0.435
15.43
0.443
0.441
Mvsplat (hen et al. 2024b)
14.96
0.341
0.401
13.73
0.253
0.419
MVSgausstan Liu et al. 2025
14.28
0.528
0.531
RRobustNeRF Sabour ct al.
2023
15.98
0.390
0.372
14.61
0.276
0.445
On-the-go (Ren ct al. 2024
14.67
0.354
0.432
14.23
0.287
0,436
+ *NeRF-HuGiS (Chen et al.
2024a)
19.02
0.699
0.250
15.26
0.463
0.355
+SLS (Sabour et al. 2024
19.28
0.716
0.227
15.14
0.443
0.388
DGGS-TR
20.32
0.737
0.214
16.37
0.492
0.337
Table 13: Ablations for Reference Scoring.
Mean (RobustNeRF)
Methods
PSNR
SSIM
LPIPS
DGGS-TR (N==)
21.02
0.738
0.242
DGGS-TR (N=8)
20.22
0.690
0.311
DGGS w/o Pruning (N=4)
21.47
0.749
0.242
B.7 QUALITATIVE COMPARISON OF PREDICTED MASKS
As shown in Fig. 16 our comparison between generalizable mask predictions, Robust Masks, and scene-specific trained masks reveals DGGS's capability to climinate over-predicted regions from initial Robust Masks while achieving accurate results without scene-specific training. The predicted masks exhibit, unexpectedly, performance levels that rival scene-specific training approaches. Note that for fair evaluation, masks are predicted under DGGS-TR without the involvement any inference framework.
Our reference- based prediction proves effective for distractor mask estimation, an approach rarely addressed in previous distractor-free works. While NeRF-HuGS (Chen et al. (2024b introduced simi- lar concepts using SiM-based Heuristics, their approach relied heavily on predefined scene Colmap rather than direct reference inference, presenting substantial challenges in generalizable settings. Drawing inspiration from human perception, we observe that humans naturally identify transient objects (or distractor) across N references from arbitrary scenes by leveraging 3D consistency to establish correspondences between different viewpoints. In this process, distractor typically manifest in regions exhibiting inconsistent correspondence relationships. Capitalizing insight, DGGS identifies non- non-distractor regions scene-ase agnostically through the exploitation of stable references residual loss and 3D consistency of static regions. This process, detailed in Sec. 1.1.I. effectively functions as filtering mechanism for the Robust Masks based on references, establishing a novel paradigm for eeferences-basee distractor mask prediction.
this fundamental
B.8 QUALITATIVE COMPARISON IN MORE SYNTHETIC SCENES
We report more results on synthetic scenes Fig. To avoid the impact of selecting different references, here we compare the qualitative results of Mvsplat and DGGS-TR under the same references. not difficult to notice that Mvsplat* (Pretrain) is severely affected by distractor, especially with the presence of artifacts and blurring. This blurring even appears in some non- distractor areas, suggesting that the 3D inconsistencies might have influenced the overall image encoding before decoding. Our DGGS-TR effectively mitigates these issues under the same references, but some artifacts still exist in certain cases (in the third and fifth rows). Then introducing an additional inference framework effectively alleviates this problem.
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Robust Mask (Ma)
NeRF-HUGS
Qurs
Robustt Misk (MRr)
NeRF-HUGS (Seme-specili))
Durs
Images
(Sone-specilic)
Images
(MD
Figure 12: Qualitative Comparison for our scene-agnostic masks prediction vs. Robust Mask and Scene-specific training results.
Mosglai*SSLSFFT
DGGS--TR DGUSSTTT-LLIGS-F FT
Mvsplac's SL. DGGS- DDGGS-FF
Figure 13: Qualitative Experiments for the Fine-tuning Model.
B.9 QUALITATIVE COMPARISON FINE-TUNING MODEL
Fig. 13 reports the qualitative results of the fine-tuned model. Compared to the fine-tuning results of the SLS (Sabour et al., 2024) distractor masking method on the pre-trained Mvsplat DGGS- *R* TR*+DGGS-FT demonstrates better results, which consistent with Tab. 3. This is mainly because DGGS achieve more accurate distractor prediction, making the training process more stable.
B.10 SINGLE-SCENE TRAINING SETTING
In addition to generalization capabilities, we investigate the performance of our reference-based paradigm in single-scene training configurations (Bao et al. 2023b).
Following distractor-fret experimental protocols (Sabour et al.
2023 2024 Chen al.) 2024a).
Tab. pressenss the performance of scene-specific trained DGGS- TR on the statue scene, trained on distractor-rich data ('clutter') without access to distractor-fres references during inference 'extra'. Notably, DGGS achieves comparable results existing approaches despite the absence of explicit iterative optimized representations. Moreover, when simulating practical scenarios by introducing partial distractor-fe free references during inference (while maintaining partial distractor rich refer- ences, corresponding to the bottom row of the Tab. 7 DGGS exhibits superior performance. This performance be primarily attributed to DGGS's accurate distractor prediction capabilities.
C FAILURE CASES
Fig. 16 presents analysis of failure cases. As mentioned in Sec. 6, although DGGS effectively mitigates distractor effects during both training and inference phases, encounters difficulties with consistently occluded regions occluded by robots that are not visible in other views (left) and areas consistently blocked by a car in the lower-left corner (right).
reference views and novel areas. Examples include regions
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Pixelspaa
Mysplat
Mespllte On the
Musplat= RobusiNeRF
Mosglat= NeRF-LIUGS
Mysplate SLS
DGGS-TR
Figure 14: More Qualitative Comparison of Re-trained Existing Methods across unseen scenes.
(Peeraa))Peerrin))
DOGS TR
DGGS
GT
Mispplr* (Pretrain)
DGGS-TR
DGGS
GT
Figure 15: More Qualitative Comparison of Pre-trained Models and our DGGS-TR as well as DGGS under unseen scenes.
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Reference Images
Reference Images
Inference
GT
Inference
GT
Figure 16: Failure Cases.
22


Reviews:
[{"review_id": "HfNGyybWIH", "reviewer": "Reviewer_KDhL", "summary": "This submission introduces DGGS, a new framework for Distractor-free Generalizable 3D Gaussian Splatting. It tackles two overlooked issues in generalizable 3DGS: (1) training instability due to transient distractors in real-world data, and (2) feed-forward inference artifacts caused by distractors in references. The method proposes a reference-based mask prediction that leverages 3D multi-view consistency to filter robust residual-based masks, a mask refinement stage that decouples disparity-induced errors and uses entity segmentation plus an auxiliary loss, and a two-stage inference procedure with reference scoring and distractor pruning. Extensive experiments on real (On-the-go, RobustNeRF) and synthetic data show consistent improvements over retrained generalizable baselines and scene-specific distractor-free approaches adapted to the generalizable setting, with additional gains from the inference stage.", "questions": "(1) How sensitive is performance to the quality of the pre-trained segmentation model and its domain shift (e.g., indoor vs. outdoor, low light)? Can lighter/zero-shot segmenters maintain most gains?\n\n(2) Why choose strict intersection for multi-view mask fusion? Have you tried soft/weighted fusion (e.g., confidence weighting by photometric residuals or view angle) to recover more static pixels without raising distractor leakage?\n\n(3) Can the auxiliary loss be extended with photometric/feature consistency terms to lessen dependence on segmentation?\n\n(4) How robust is the approach when the majority of references contain similar distractors (e.g., many frames with the same moving car)? Does reference scoring still find sufficiently clean views?\n\n(5) For pruning, did you evaluate per-primitive confidence aggregation across references (e.g., voting) instead of binary masking per view to reduce speckle?\n\n(6) Could you report memory/time breakdown across stages (feature projection, mask prediction/refinement, scoring, pruning) and how they scale with K and N?\n\n(7) Are there benefits or risks in training with the scoring mechanism online (curriculum-style selection of “cleaner” references) rather than only at inference?\n\n(8) How does DGGS perform when camera intrinsics vary or are noisy? Is U assumed known and consistent?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clearly identifies and formulates a new, practically relevant problem: distractor-free generalizable 3DGS.\n\n- Elegant reference-based mask filtering that reduces over-suppression typical of residual-only masks.\n\n- Thoughtful mask refinement: decoupling disparity vs. distractor; auxiliary loss exploiting cross-view occlusion cues.\n\n- Practical two-stage inference: reference scoring and 3D primitive pruning demonstrably reduce artifacts/holes.\n\n- Strong empirical results with comprehensive comparisons, ablations, and both real and synthetic setups.\n\n- Method is modular and can plug into existing generalizable 3DGS pipelines.", "weaknesses": "(1) Dependence on pre-trained entity segmentation during training and inference undermines full “feed-forward” purity and adds latency; domain robustness of the segmenter is not analyzed.\n\n(2) The mask fusion strategy uses intersection across references (conservative), which may lead to under-coverage in low-overlap or high-parallax settings; the trade-off is not deeply quantified.\n\n(3) Distractor pruning can introduce speckle/holes in commonly occluded areas; mitigation is heuristic and the failure modes are only briefly discussed.\n\n(4) Some reliance on depth/warping quality from inferred 3DGS; failure cases when depth is noisy or textures are repeated are not thoroughly dissected.\n\n(5) Fairness concerns: scene-specific methods are adapted into a generalizable training loop but may not reflect their best practices (e.g., stronger per-scene optimization), making cross-paradigm comparisons tricky.\n\n(6) Efficiency overhead from two-stage inference and segmentation is non-trivial; the paper reports times but not detailed profiling or memory usage under varied K, N, and resolution.", "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": "rErWuM9ohr", "reviewer": "Reviewer_sapT", "summary": "This paper proposes a framework called DGGS aimed at making generalizable 3D Gaussian Splatting (3DGS) robust against distractors (transient objects) in real-world scenes. Existing generalizable 3DGS models assume static environments and suffer from training instability and artifacts when transient objects appear. DGGS mitigates this by predicting distractor masks through multi-view geometric consistency and refining them with segmentation priors, then using these masks to exclude distractors during training. At inference, it selects cleaner reference views and prunes distractor-related Gaussian primitives. Experiments demostrate this method outperform the generalizable 3DGS baselines, even better than some scene-specific distractor removal techniques.", "questions": "Out of curiosity, can the two-stage inference be made more efficient for real-time use? Have you tested foundation segmentation models like SAM-2, and do they meaningfully improve results?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- This is the first work addressing distractors in generalizable 3DGS, filling an important gap in real-world usage.\n- It significantly boosts robustness and reconstruction quality compared to both baseline 3DGS models and naively transferred scene-specific distractor-free methods.\n- The approach generalizes well to unseen scenes and improves inference quality via smart reference selection and pruning.", "weaknesses": "- The method relies on several additional modules, but the sensitivity of the overall performance to the choice or quality of these modules is not discussed.\n- The quality of the generated masks depends on the accuracy of segmentation and depth estimation, which may lead to failure cases in scenes with heavy occlusions or imprecise geometry.\n- Since the approach depends on mask generation, it is unclear how well it would handle naturally dynamic environments, such as moving trees or water, where mask accuracy could be compromised.", "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": "8B6sqVHaLG", "reviewer": "Reviewer_QPBd", "summary": "The paper proposes DGGS, a framework designed to enable generalizable 3D Gaussian Splatting under distractor-rich real-world scenarios. Unlike existing generalizable 3DGS methods that assume clean static scenes, this work introduces: 1) A Reference-based Mask Prediction mechanism leveraging multi-view consistency. 2) A Mask Refinement module using segmentation priors and occlusion-aware auxiliary supervision. 3) A Two-stage inference strategy, including reference scoring and 3D gaussian primitive pruning, to suppress inference-time artifacts.", "questions": "1. How robust is the reference mask filtering when references also contain distractors?\n\n2. What is the computational overhead of the full pipeline?\n\n3. Does the segmentation model need retraining or domain adaptation in unseen categories?\n\n4. Could the mask refinement be done without segmentation, e.g., self-supervised feature aggregation?\n\n5. How does performance degrade with increasing viewpoint disparity among references?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The use of multi-view geometric consistency to correct masks, reflects good insight rather than brute force.\n\n2. Comprehensive experiments, including synthetic distractor construction.\n\n3. Inference-time pruning is practical and effective.", "weaknesses": "1. Heavy reliance on segmentation priors. The pipeline is not truly “feed-forward generalizable” if high-quality segmentation is required and pre-computed.\n\n2. Reference stability assumption unproven. The paper does not quantify how often reference re-rendering is accurate enough to serve as a stable supervisory source.\n\n3. Efficiency cost. Two-stage inference + segmentation noticeably sacrifices speed, which is a key appeal of 3DGS.\n\n4. Mask failure modes not fully analyzed. The limitations section mentions occlusions, but no systematic characterization is provided.", "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": "FQxlL9ULHE", "reviewer": "Reviewer_ArTN", "summary": "The authors propose Distractor-free Generalizable 3D Gaussian Splatting, a method designed for removing transient objects in feedforward GS methods. The main goal is to find proper transient masks and remove transients from optimizing the feedforward network. Firstly, DGGS implemented a robust mask based on robustnerf. Such mask is improved by incorporating a mask_ref (from re-rendered photometric loss) from reference images and project it on the target pose. Such mask is further improved based on multi-view visibility and combines with robustnerf. At test time, DGGS introduces schemes to reduce the usage of images with strong transient objects, and perform additional optimization to remove artifacts in the scene.", "questions": "1. The image resolution in the submitted PDF is really low. To the point where this is difficult to read. \n2. Given that NeRF-HUGS produces similar masks as DGGS, can the authors expand on why mvsplat + NeRF-HUGS is significantly worse than DGGS?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. The authors demonstrated superior performances in feedforward 3DGS compared to prior work based on PSNR/SSIM etc.\n3. The masks seem reasonable, and the resultant visual quality is also improved.", "weaknesses": "In general, I think the writing requires a lot of polishing, in terms of readability, background knowledge, e.g., segmentation models which play a significant role, and overall importance/novelty of the method. \n\nAfter reading this work, the sense that I get is that, while performance is certainly good, the paper heavily leans on engineering and parameter tuning. This includes multiple important hyperparameters (threshold for masks), requirement for estimated depth to be reasonable for reprojection, integration of an external segmentation model that is minimally mentioned, test time hyperparameters (N views - BTW, N is defined multiple times with different meanings across the text). The overall insight is not very different from prior work, i.e., finding good transient masks such that rendering results can be improved by ignoring transients. I also have concerns about how this can be applied to larger scale scenes, as feedforward GS currently is relatively limited. Large scale scenes will lead to more noise in depth and multi-view inconsistencies, which this method seems to be very sensitive on. \n\nI lean borderline on this work based on novelty; since there is no borderline, I lean towards borderline reject as I believe the writing can be better.", "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)