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arxiv: 2604.12580 · v2 · submitted 2026-04-14 · 💻 cs.CV

PDF-GS: Progressive Distractor Filtering for Robust 3D Gaussian Splatting

Kangmin Seo , MinKyu Lee , Tae-Young Kim , ByeongCheol Lee , JoonSeoung An , Jae-Pil Heo This is my paper

Pith reviewed 2026-05-10 14:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingdistractor filteringprogressive optimizationrobust 3D reconstructionmulti-view consistencyphotorealistic renderingscene reconstruction from images
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The pith

Progressive multi-phase optimization amplifies 3D Gaussian Splatting's built-in suppression of inconsistent image signals to produce distractor-free reconstructions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that 3D Gaussian Splatting can be made robust to input images containing distractors like moving objects by deliberately amplifying the method's natural tendency to ignore signals that fail multi-view consistency checks. It does so through an iterative schedule of filtering phases that use discrepancy cues to purge inconsistent Gaussians, followed by reconstruction phases that recover high-frequency details from the cleaned set. A reader would care because standard 3DGS training collapses into artifacts whenever the input collection violates the perfect-consistency assumption that is common in uncontrolled photography. If the claim holds, practitioners could train high-quality 3D models directly on raw photo sets without manual masking or separate preprocessing pipelines. The approach adds no new network layers and imposes no extra cost once training finishes.

Core claim

PDF-GS performs progressive distractor filtering by alternating filtering phases that exploit view-discrepancy cues to remove inconsistent Gaussians with reconstruction phases that restore view-consistent detail from the purified representation. This iterative refinement yields robust, high-fidelity, distractor-free 3D reconstructions that outperform baselines on diverse datasets and real-world conditions while remaining fully compatible with existing 3DGS pipelines and incurring no architectural changes or inference overhead.

What carries the argument

Progressive multi-phase optimization schedule that alternates discrepancy-driven distractor removal with detail-restoring reconstruction to amplify 3DGS self-filtering of inconsistent signals.

If this is right

  • Existing 3DGS implementations can be made robust to distractors by inserting the progressive filtering schedule with no code changes to the core renderer.
  • Training produces high-fidelity models directly from raw photo collections that contain moving objects or lighting inconsistencies.
  • Inference speed and memory footprint remain identical to standard 3DGS because no extra components are added.
  • The same schedule delivers consistent gains across indoor, outdoor, and real-world capture conditions without dataset-specific tuning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same progressive filtering idea could be tested on other radiance-field methods that also exhibit implicit regularization against view inconsistency.
  • It may reduce reliance on upstream object detectors or mask generators that are currently used to clean training images.
  • One could measure whether the filtering phases also suppress other common inconsistencies such as specular highlights or shadows that vary across views.

Load-bearing premise

3D Gaussian Splatting possesses a self-filtering property for inconsistent signals that can be reliably strengthened through progressive phases without losing fine details or introducing new artifacts.

What would settle it

Run the method on a controlled dataset of multi-view images containing a known moving foreground object; if the final rendered views still show ghosting or blurring of that object, or if fine background geometry is degraded relative to plain 3DGS, the central claim does not hold.

Figures

Figures reproduced from arXiv: 2604.12580 by ByeongCheol Lee, Jae-Pil Heo, JoonSeoung An, Kangmin Seo, MinKyu Lee, Tae-Young Kim.

Figure 1
Figure 1. Figure 1: Overview of PDF-GS. During the Progressive Filtering Phases (phases 1–3), PDF-GS progressively removes transient, view￾inconsistent distractors. Across successive phases, inconsistent regions are suppressed while stable, view-consistent structures are pre￾served. In the final Reconstruction Phase (phase 4), fine-grained appearance details are recovered from the purified representation, leading to a high-fi… view at source ↗
Figure 2
Figure 2. Figure 2: Self-filtering behavior of vanilla 3DGS. After stan￾dard 3DGS training, distractor objects visible in the train view (a) are removed in the rendered result (b), indicating that inconsistent regions tend to either disappear or become blurred in the recon￾struction (Crab2 scene in RobustNeRF dataset [20]). for fine-grained details, while maintaining the discrepancy￾based distractor mask to prevent the reacti… view at source ↗
Figure 3
Figure 3. Figure 3: Conceptual illustration of PDF-GS. Our method progressively filters out transient and view-inconsistent distractors through iterative refinement. During the filtering phases, regions exhibiting multi-view inconsistencies are identified by the discrepancies between rendered views and training images. These inconsistent regions are then masked out, while stable view-consistent regions exhibiting small discre… view at source ↗
Figure 4
Figure 4. Figure 4: Effectiveness of the structure-oriented objective (Eq. 4). The model trained with standard 3DGS loss (b) over￾fits transient distractors and exhibits color bleeding, while using structure-oriented objective (c) preserves structural consistency and better suppresses distractors (Patio scene in NeRF On-the￾go [18]). the Reconstruction Phase at phase k = K. The Reconstruction Phase then generates the final 3D… view at source ↗
Figure 5
Figure 5. Figure 5: Analysis on the number of filtering phases. Quantita￾tive results show a gradual increase in reconstruction scores as the number of filtering phases increases, with performance saturating around three phases (NeRF On-the-go dataset [18]). 4.3. Ablation Study To better understand the contribution of each component in our framework, we conduct a series of ablation studies on the NeRF On-the-go dataset. Unles… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results of PDF-GS (Ours) and baseline State-of-the-Art methods. Our method generates noticeably fewer distractor-induced artifacts and more accurate reconstruction of static objects and backgrounds than previous methods. As the number of filtering phases increases, performance consistently improves because each phase further removes residual distractors that remain from previous stages. The mos… view at source ↗
Figure 7
Figure 7. Figure 7: Effect of re-initialization between filtering phases. (a) Without re-initialization. Accumulated errors propagate across filtering iterations and appear as persistent artifacts. (b) With re￾initialization (Ours). Such error buildup is avoided, resulting in a cleaner and more stable reconstruction. (Corner scene in NeRF On-the-go dataset [18]). 4.3.3. Gradually Decreasing Threshold. During the progressive f… view at source ↗
Figure 8
Figure 8. Figure 8: visualizes how per-view masks evolve across our pro￾gressive filtering phases. In the first phase, distractors have not yet been removed, which leads to supervision that is noticeably noisier than in later stages. To avoid discarding valid content under this noisy setting, we adopt a conserva￾tive masking threshold that filters out only regions showing strong discrepancy signals. Train View Filtering Phase… view at source ↗
read the original abstract

Recent advances in 3D Gaussian Splatting (3DGS) have enabled impressive real-time photorealistic rendering. However, conventional training pipelines inherently assume full multi-view consistency among input images, which makes them sensitive to distractors that violate this assumption and cause visual artifacts. In this work, we revisit an underexplored aspect of 3DGS: its inherent ability to suppress inconsistent signals. Building on this insight, we propose PDF-GS (Progressive Distractor Filtering for Robust 3D Gaussian Splatting), a framework that amplifies this self-filtering property through a progressive multi-phase optimization. The progressive filtering phases gradually remove distractors by exploiting discrepancy cues, while the following reconstruction phase restores fine-grained, view-consistent details from the purified Gaussian representation. Through this iterative refinement, PDF-GS achieves robust, high-fidelity, and distractor-free reconstructions, consistently outperforming baselines across diverse datasets and challenging real-world conditions. Moreover, our approach is lightweight and easily adaptable to existing 3DGS frameworks, requiring no architectural changes or additional inference overhead, leading to a new state-of-the-art performance. The code is publicly available at https://github.com/kangrnin/PDF-GS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that conventional 3D Gaussian Splatting is sensitive to distractors violating multi-view consistency, but 3DGS has an inherent self-filtering property for inconsistent signals. PDF-GS amplifies this through progressive multi-phase optimization: filtering phases use discrepancy cues to remove distractors, followed by reconstruction phases to restore fine details. This leads to robust, high-fidelity, distractor-free reconstructions that consistently outperform baselines on diverse datasets, with the method being lightweight, adaptable to existing 3DGS frameworks without architectural changes or inference overhead, achieving new state-of-the-art performance.

Significance. Should the empirical validation confirm the claims, this work would be significant for the 3D reconstruction community by offering a practical, training-only solution to a prevalent issue with real-world captures containing distractors. The lack of additional inference cost and easy integration are strong points. Public code availability aids reproducibility and adoption.

major comments (2)
  1. The central premise that progressive multi-phase optimization can amplify the self-filtering property without losing fine details or introducing artifacts is load-bearing for the main contribution. The method section should provide explicit definitions of discrepancy cues and the phase transition criteria to allow assessment of whether the approach is reliable.
  2. The claim of consistent outperformance and SOTA requires detailed quantitative results, including specific metrics on multiple datasets, comparisons to baselines, and ablations on the number of phases and their impact on reconstruction quality to support the assertions.
minor comments (1)
  1. The abstract could include brief mention of key quantitative improvements or dataset names to better convey the strength of the results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the practical value of our approach. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: The central premise that progressive multi-phase optimization can amplify the self-filtering property without losing fine details or introducing artifacts is load-bearing for the main contribution. The method section should provide explicit definitions of discrepancy cues and the phase transition criteria to allow assessment of whether the approach is reliable.

    Authors: We agree that explicit definitions are necessary for rigorous assessment. The current manuscript describes discrepancy cues as signals of multi-view inconsistency detected via rendering discrepancies and phase transitions as occurring upon stabilization of the Gaussian set. To strengthen this, we will revise the Method section to include formal definitions, mathematical formulations of the cues, and precise transition criteria (e.g., iteration-based or discrepancy-threshold triggers), along with pseudocode. This will clarify how the progressive process preserves details without introducing artifacts. revision: yes

  2. Referee: The claim of consistent outperformance and SOTA requires detailed quantitative results, including specific metrics on multiple datasets, comparisons to baselines, and ablations on the number of phases and their impact on reconstruction quality to support the assertions.

    Authors: The manuscript already reports quantitative results on multiple datasets using standard metrics (PSNR, SSIM, LPIPS) with comparisons to 3DGS and distractor-handling baselines, plus initial ablations. However, to more robustly support the consistent outperformance and SOTA claims, we will expand the Experiments section with additional specific metric tables, further dataset evaluations, and a dedicated ablation study varying the number of phases and measuring effects on reconstruction quality. These enhancements will be added in the revision. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical method without self-referential derivation

full rationale

The paper describes PDF-GS as a practical, iterative training procedure that amplifies an observed self-filtering behavior of 3DGS via progressive phases using discrepancy cues, followed by reconstruction. No equations, closed-form predictions, fitted parameters renamed as outputs, or first-principles derivations are presented that reduce to the method's own inputs by construction. The central claim rests on empirical validation across datasets rather than any mathematical chain that would qualify under the enumerated circularity patterns. No load-bearing self-citations or ansatzes imported from prior author work appear in the text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text. The core assumption that 3DGS can suppress inconsistent signals is treated as an empirical observation rather than a formally stated axiom.

pith-pipeline@v0.9.0 · 5540 in / 1254 out tokens · 37994 ms · 2026-05-10T14:55:53.155380+00:00 · methodology

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