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arxiv: 2604.20155 · v2 · pith:H2VWPK4Dnew · submitted 2026-04-22 · 💻 cs.CV

GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds

Ao Gao , Jingyu Gong , Xin Tan , Zhizhong Zhang , Lizhuang Ma , Yuan Xie This is my paper

Pith reviewed 2026-05-21 00:31 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingscene completiondistillation-freemetric-awareray-constrained registrationneural renderingsparse viewpoints
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The pith

GSCompleter completes 3D Gaussian Splatting scenes from sparse views by generating and registering metric-scale Gaussians instead of using distillation.

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

The paper presents GSCompleter as a plugin that fixes incomplete 3D Gaussian Splatting reconstructions caused by sparse camera coverage. It avoids the slow and unstable repair-then-distill approach by first synthesizing 2D reference images from the available views. These images are lifted into 3D Gaussian primitives that maintain a consistent metric scale. The new primitives are then added to the scene through a ray-constrained registration process. This results in faster completion times and better performance on standard benchmarks compared to existing methods.

Core claim

By replacing unstable distillation with rapid geometric registration, GSCompleter exhibits superior 3DGS completion performance across three benchmarks, enhancing both quality and efficiency over various baselines and achieving new state-of-the-art results. The method synthesizes visually plausible 2D reference images and explicitly lifts them into 3D Gaussian primitives with a consistent metric scale via a robust Stereo-Anchor View Selection mechanism before integrating them using Ray-Constrained Registration.

What carries the argument

The Ray-Constrained Registration strategy, which integrates newly generated 3D Gaussian primitives into the global scene while enforcing geometric consistency through ray constraints.

Load-bearing premise

Synthesized 2D reference images can be lifted into 3D Gaussian primitives with consistent metric scale and then integrated via ray-constrained registration without introducing geometric inconsistencies or visual artifacts in the global scene.

What would settle it

If rendering the completed scene from viewpoints outside the original sparse set and the synthesized references reveals persistent floaters or scale inconsistencies, the effectiveness of the registration strategy would be disproven.

Figures

Figures reproduced from arXiv: 2604.20155 by Ao Gao, Jingyu Gong, Lizhuang Ma, Xin Tan, Yuan Xie, Zhizhong Zhang.

Figure 1
Figure 1. Figure 1: Overview of GSCompleter. We propose a “Generate￾then-Register” paradigm for rapid and robust 3DGS scene com￾pletion. (a) Given a 3DGS scene exhibiting geometric voids, (b) we first synthesize a high-fidelity 2D reference image via a gen￾erative prior and explicitly lift it into metric-scale 3D Gaussian primitives guided by a stereo anchor view. (c) Instead of global optimization, we seamlessly register the… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the GSCompleter. Addressing the geometric holes in the novel view, we adopt a “Generate-then-Register” paradigm to complete the scene via four stages: (1) Feed-Forward Metric Context Initialization: We first reconstruct the observed regions using a scale-aware feed-forward 3DGS model, establishing a foundational context with metric scale; (2) Anchor-Guided Gaussian Initialization: To fill the v… view at source ↗
Figure 3
Figure 3. Figure 3: Stereo-Anchor Selection Strategy. We identify the optimal reference for 3D lifting through a prioritized process: (1) Filtering: Context views with relative rotation ∆θ > 45◦ are discarded to ensure sufficient overlap. (2) Selection: Among valid candidates (Left), we select the one with the maximum baseline to stabilize metric scale. (3) Fallback: In extreme cases where no candidates satisfy the angular co… view at source ↗
Figure 4
Figure 4. Figure 4: Ray-Constrained Gaussian Registration. (a) Coarse Global Alignment: We employ RANSAC to estimate the global affine parameters (s, t), which are used to explicitly re-initialize the depth of the target Gaussians. (b) Fine-grained Ray-Constrained Optimization: We optimize the Gaussian depth solely by adjusting the distance along the camera ray. Concurrently, we reproject these primitives into the stereo anch… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Comparison on RealEstate10K. While baselines exhibit significant geometric collapses or “black holes” in unobserved regions, our method achieves high fidelity consistent with the Ground Truth (GT). GSCompleter accurately recovers complex geometric structures and scene details while maintaining robustness across diverse baseline architectures (e.g., pixel-aligned and voxel-aligned). 4. Experimen… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison between our method and the densification strategy. While densification tends to overfit the reference view, our method effectively mitigates this issue [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visual comparison with RegGS. RegGS suffers from severe geometric distortions (highlighted in red) arising from scale￾agnostic optimization. In contrast, our method leverages metric priors to achieve precise alignment while strictly preserving struc￾tural fidelity. Given the same input views, our geometric pipeline accelerates the process by over 170× compared to RegGS (1.43s vs. ∼4 min). itives via the ex… view at source ↗
Figure 8
Figure 8. Figure 8: Visual illustration of the 2-view input and 1-view target extrapolation setting. A.2. Robustness Analysis on Extrapolation Span Following the n-k protocol established in Sec. 4, we conduct a stress test on the DL3DV dataset to evaluate model stability under extreme sparsity. As the frame interval k increases, the baseline distance between context views expands, leading to a drastic reduction in visual over… view at source ↗
Figure 9
Figure 9. Figure 9: and [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative comparison on long sequences. RegGS suffers from progressive blurring and structural drift due to metric scale instability. In contrast, GSCompleter maintains sharp details and global consistency, effectively rectifying artifacts in challenging frames. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: More results on the RealEstate10K dataset. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: More results on the ACID dataset. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: More results on the DL3DV dataset. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
read the original abstract

3D Gaussian Splatting (3DGS) has revolutionized high-fidelity neural rendering with its explicit representation and efficiency. However, reconstructing scenes from sparse viewpoints suffers from severe geometric voids and floaters due to limited coverage. Current scene completion methods typically rely on an iterative "Repair-then-Distill" paradigm, which is computationally intensive, prone to unstable optimization, and susceptible to overfitting. To address these limitations, we propose GSCompleter, a distillation-free plugin that shifts scene completion to a stable "Generate-then-Register" workflow. Specifically, GSCompleter synthesizes visually plausible 2D reference images and explicitly lifts them into 3D Gaussian primitives with a consistent metric scale via a robust Stereo-Anchor View Selection mechanism. These newly generated primitives are then seamlessly integrated into the global scene using a novel Ray-Constrained Registration strategy. By replacing unstable distillation with rapid geometric registration, GSCompleter exhibits superior 3DGS completion performance across three benchmarks, enhancing both quality and efficiency over various baselines and achieving new state-of-the-art (SOTA) results.

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

3 major / 2 minor

Summary. The paper proposes GSCompleter, a distillation-free plugin for 3D Gaussian Splatting (3DGS) scene completion. It replaces the iterative 'Repair-then-Distill' paradigm with a 'Generate-then-Register' workflow: synthesizing 2D reference images, lifting them to metric-scale 3D Gaussian primitives via Stereo-Anchor View Selection, and integrating them into the existing scene using Ray-Constrained Registration. The method claims superior quality and efficiency over baselines, achieving new SOTA results on three benchmarks.

Significance. If the geometric consistency claims hold, GSCompleter could offer a practical efficiency gain for 3DGS completion by avoiding unstable optimization loops, with potential benefits for sparse-view reconstruction tasks. The explicit registration approach may scale better than distillation methods, though its impact depends on validation of the metric-scale lifting step.

major comments (3)
  1. [Stereo-Anchor View Selection mechanism] The central performance claims rest on the assumption that Stereo-Anchor View Selection produces 3D Gaussians with metric scale exactly matching the input scene. The manuscript should include quantitative validation (e.g., scale-error metrics or ablation on depth estimation accuracy from synthesized images) in the section describing this mechanism, as depth ambiguities in generative 2D synthesis could propagate to floaters or inconsistencies after Ray-Constrained Registration.
  2. [Ray-Constrained Registration strategy] Ray-Constrained Registration is presented as the integration step that avoids geometric artifacts. The paper needs to report specific metrics (e.g., PSNR/SSIM differences with and without the ray constraint, or failure cases on regions with high depth variance) to substantiate that it corrects rather than masks underlying scale or distortion errors from the lifting stage.
  3. [Experimental results] The abstract and results sections assert SOTA performance across three benchmarks with no quantitative tables, error bars, or per-scene breakdowns visible in the provided description. Full experimental results must include direct comparisons to distillation baselines with standard metrics and statistical significance to support the efficiency and quality superiority claims.
minor comments (2)
  1. [Method overview] Clarify the exact number and selection criteria for the synthesized reference views in the Stereo-Anchor mechanism to improve reproducibility.
  2. [Figures] Ensure all figures showing completed scenes include side-by-side comparisons with ground truth or baseline outputs for visual assessment of artifacts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our work. We have carefully addressed each major comment below with point-by-point responses. Where the suggestions strengthen the validation of our claims, we have incorporated revisions into the manuscript.

read point-by-point responses

  1. Referee: [Stereo-Anchor View Selection mechanism] The central performance claims rest on the assumption that Stereo-Anchor View Selection produces 3D Gaussians with metric scale exactly matching the input scene. The manuscript should include quantitative validation (e.g., scale-error metrics or ablation on depth estimation accuracy from synthesized images) in the section describing this mechanism, as depth ambiguities in generative 2D synthesis could propagate to floaters or inconsistencies after Ray-Constrained Registration.

    Authors: We agree that additional quantitative validation of metric-scale consistency would further strengthen the paper. In the revised manuscript, we have added a dedicated analysis subsection that reports scale-error metrics (mean absolute scale deviation against ground-truth depths) across the three benchmarks. We also include an ablation examining depth estimation accuracy from the synthesized reference images and its downstream effect on completion quality, confirming that the Stereo-Anchor View Selection effectively resolves scale ambiguities without introducing floaters. revision: yes

  2. Referee: [Ray-Constrained Registration strategy] Ray-Constrained Registration is presented as the integration step that avoids geometric artifacts. The paper needs to report specific metrics (e.g., PSNR/SSIM differences with and without the ray constraint, or failure cases on regions with high depth variance) to substantiate that it corrects rather than masks underlying scale or distortion errors from the lifting stage.

    Authors: We appreciate this suggestion for stronger substantiation. The revised experimental section now contains an ablation study that directly compares PSNR and SSIM with and without the ray constraint, showing consistent gains in geometric fidelity. We further analyze and report failure cases on high depth-variance regions, demonstrating that the constraint actively corrects scale and distortion errors from the lifting stage rather than simply masking them. revision: yes

  3. Referee: [Experimental results] The abstract and results sections assert SOTA performance across three benchmarks with no quantitative tables, error bars, or per-scene breakdowns visible in the provided description. Full experimental results must include direct comparisons to distillation baselines with standard metrics and statistical significance to support the efficiency and quality superiority claims.

    Authors: The full manuscript already presents comprehensive quantitative tables with direct comparisons to distillation baselines using PSNR, SSIM, and LPIPS, including per-scene breakdowns on all three benchmarks. To address the referee's request, we have added error bars to the aggregate results and included statistical significance testing (paired t-tests) between GSCompleter and the strongest baselines. These enhancements are now explicitly highlighted in the results section. revision: partial

Circularity Check

0 steps flagged

No circularity: method relies on independent geometric registration pipeline

full rationale

The paper describes a Generate-then-Register workflow that synthesizes 2D reference images, lifts them to metric-scale 3D Gaussians using Stereo-Anchor View Selection, and integrates via Ray-Constrained Registration. No equations, fitted parameters, or self-referential definitions appear in the abstract or described claims that would make any output equivalent to the input by construction. The approach is presented as an alternative to distillation-based methods and is evaluated against external baselines on three benchmarks, rendering the derivation self-contained without load-bearing self-citations or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are quantified, but the method introduces two new algorithmic components whose details and assumptions are not provided.

invented entities (2)
  • Stereo-Anchor View Selection no independent evidence
    purpose: Selects views to lift 2D images into 3D Gaussians with consistent metric scale
    New mechanism described in the proposed workflow
  • Ray-Constrained Registration no independent evidence
    purpose: Integrates generated 3D primitives into the global scene
    Novel strategy replacing distillation

pith-pipeline@v0.9.0 · 5736 in / 1217 out tokens · 38058 ms · 2026-05-21T00:31:17.362106+00:00 · methodology

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discussion (0)

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Reference graph

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8 extracted references · 8 canonical work pages · 2 internal anchors

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