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arxiv: 2605.30987 · v1 · pith:ZA2FXOTPnew · submitted 2026-05-29 · 💻 cs.CV

Benchmarking Single-Step Inpainting Methods for Multi-Object 3D Gaussian Splatting Scenes

Finn Dr\"oge , Cecilia Curreli , Abhishek Saroha , Daniel Cremers This is my paper

Pith reviewed 2026-06-28 22:49 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattinginpaintingobject removal3D consistencyreconstruction-based inpaintingmulti-object scenesground truth dataset
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The pith

Reconstruction-based inpainters outperform generative diffusion models in 3D consistency for Gaussian Splatting object removal, and scene initialization from scratch beats finetuning.

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

This paper benchmarks how 2D inpainting methods can be used for removing objects from 3D Gaussian Splatting scenes while keeping the results consistent from all camera angles. Reconstruction-based inpainters prove more effective than generative diffusion models at maintaining that 3D consistency. The authors also compare ways to build or update the 3D scene after inpainting the 2D images, finding that starting a fresh scene produces better results than adjusting the original one. They support their comparisons with a new dataset of multi-object scenes that includes real ground truth data and many views where objects are occluded.

Core claim

The central claim is that reconstruction-based inpainters outperform generative diffusion models in 3D consistency. Integrating 2D inpainters into single-step 3DGS methods shows that initializing the scene from scratch produces higher quality results than finetuning the existing scene. A baseline using a generative 2D inpainter highlights the need for object removal prior to inpainting. The authors also present a new multi-object scene dataset with recorded ground truth and occlusion views.

What carries the argument

Single-step methods for integrating 2D inpainters into 3D Gaussian Splatting scenes, including the choice between reconstruction-based and generative approaches and between initializing from scratch versus finetuning.

Load-bearing premise

That the performance differences observed on the authors' new multi-object scene with ground truth will generalize to other 3D Gaussian Splatting scenes and that the chosen single-step integration methods fairly represent the space of possible 3D inpainting pipelines.

What would settle it

A comparison on additional 3DGS scenes where generative diffusion models achieve higher 3D consistency scores than reconstruction-based ones, or where finetuning an existing scene yields better quality than initializing from scratch, would disprove the central claims.

Figures

Figures reproduced from arXiv: 2605.30987 by Abhishek Saroha, Cecilia Curreli, Daniel Cremers, Finn Dr\"oge.

Figure 1
Figure 1. Figure 1: Overview of the 3D Scene Inpainting Pipeline. First, a 3D Gaussian Splatting scene is initialized from input images using Gaussian Grouping [17]. Then, the object is removed using the identity encoding of the Gaussians. Using the object masks, the inpainting masks are created for the resulting hole in the scene using a method from 3DGIC [4]. Using a 2D inpainter the images and depth images are inpainted an… view at source ↗
Figure 2
Figure 2. Figure 2: 2D Inpainter Comparison. We compare the ground truth with the inpainting results from LaMa [11], PowerPaint [19], Nano Banana [20], and BrushNet [5]. We visualize the bear scene (top), the kitchen scene (middle), and our new living room scene (bottom). results of the masked metrics are very competitive. This underlines the sharp inpainting quality of Nano Banana [20] within the inpainting masks, but since … view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative Comparison of the 3D Inpainting Results. We compare the ground truth to our seven different approaches on the bear scene (top), the kitchen scene (middle), and our new living room scene (bottom). The views are all test views of the respective scene. Kitchen Inpainting Quality. The results of the 2D in￾painter are accurately reflected in 3D. Init-NanoBanana pro￾duces the highest quality results,… view at source ↗
Figure 4
Figure 4. Figure 4: Difficult Occlusion Scenario. All images are 3D results of Init-NanoBanana. All objects are visible in views from above (top) but disappear if they are covered in the input image (bottom). analysis of this scene shows the need for object removal before inpainting, as our straightforward Init-NanoBanana approach struggles with details behind the removed object [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: 2D Depth Inpainting Comparison. On the left is the input of the depth image after object removal. We compare the LaMa [11] inpainting results with the PowerPaint [19] output. allocated approximately 90% of the views for training and 10% for testing. For every method, we take the average score of all three scenes. We also provide a separate table with the metrics for every scene. Number of Gaussians. When c… view at source ↗
Figure 7
Figure 7. Figure 7: Ablation Study. On the top, the respective method is run without running COLMAP [10] again giving artifacts in the depth of the original bear. On the bottom, the respective method is run with an additional COLMAP [10] call giving blurry results. methods fail to place the background Gaussians in the cor￾rect depth. Bear COLMAP Ablation. Since we do not use COLMAP [10] again before running Gaussian Group￾ing… view at source ↗
Figure 8
Figure 8. Figure 8: Artifacts in 3D with PowerPaint. The 3D results of Init-PowerPaint are presented (top) with the respective 2D inpaint￾ing results of PowerPaint [19] (bottom). The yellow artifact in view A is visible in the 3D results of both view A and view B. painter produce higher quality results than those using Pow￾erPaint [19] since the latter hallucinate many artifacts. The quantitative difference between Finetune-L… view at source ↗
read the original abstract

The tasks of object removal and inpainting 3D Gaussian Splatting (3DGS) scenes face challenges such as 3D consistency across camera views. In comparing 2D inpainters and their suitability for the 3D domain, we find that reconstruction-based inpainters outperform generative diffusion models in 3D consistency. Integrating these 2D inpainters into different single-step methods for creating and finetuning 3DGS scenes, our results indicate that initializing the scene from scratch produces higher quality results than finetuning the existing scene. Using a state-of-the-art generative 2D inpainter, we create a straightforward baseline to underline the importance of object removal before inpainting in the 3D setting. Since 360{\deg} datasets rarely include real-world ground truths, and challenging occlusion scenarios are equally sparse, we introduce a novel multi-object scene with recorded ground truth data and many views with object occlusions.

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 / 2 minor

Summary. The manuscript benchmarks single-step inpainting methods for multi-object 3D Gaussian Splatting (3DGS) scenes. It claims that reconstruction-based inpainters outperform generative diffusion models with respect to 3D consistency across views, that initializing a 3DGS scene from scratch yields higher quality results than finetuning an existing scene when integrating 2D inpainters, and that object removal prior to inpainting is important. To support these comparisons the authors introduce a new multi-object scene containing recorded ground-truth data and multiple views with occlusions, addressing the scarcity of such data in existing 360° datasets.

Significance. If the empirical comparisons hold, the work supplies a new dataset with recorded ground truth that directly targets the acknowledged limitations of prior 360° collections and supplies concrete guidance on the relative merits of reconstruction versus generative 2D inpainters and of scratch versus finetune integration strategies. The explicit baseline that isolates the effect of object removal is a useful reference point for future 3D inpainting pipelines.

major comments (2)
  1. [Abstract] Abstract: the comparative claims (reconstruction-based inpainters outperform generative models; scratch initialization outperforms finetuning) are stated without any quantitative metrics, error bars, dataset cardinality, or exclusion criteria. These omissions are load-bearing for evaluating the central empirical claims.
  2. [Experimental Setup] The experimental protocol relies on a single newly collected multi-object scene. Without explicit reporting of scene count, view count, occlusion statistics, and the precise procedure used to record ground truth, it is impossible to assess whether the reported performance gaps generalize or are sensitive to post-hoc implementation choices.
minor comments (2)
  1. [Method] The description of the single-step integration methods would benefit from a concise pseudocode or diagram clarifying the exact sequence of 2D inpainting, 3DGS initialization, and any optimization steps.
  2. [Results] Table captions should explicitly state the number of runs or views averaged and whether error bars represent standard deviation or standard error.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript to strengthen clarity and evaluability of the claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the comparative claims (reconstruction-based inpainters outperform generative models; scratch initialization outperforms finetuning) are stated without any quantitative metrics, error bars, dataset cardinality, or exclusion criteria. These omissions are load-bearing for evaluating the central empirical claims.

    Authors: We agree that the abstract should provide quantitative support for the central claims. In the revised version we will incorporate key metrics (e.g., observed PSNR/SSIM differences for 3D consistency), error-bar information where applicable, the dataset cardinality, and any exclusion criteria used in the comparisons. revision: yes

  2. Referee: [Experimental Setup] The experimental protocol relies on a single newly collected multi-object scene. Without explicit reporting of scene count, view count, occlusion statistics, and the precise procedure used to record ground truth, it is impossible to assess whether the reported performance gaps generalize or are sensitive to post-hoc implementation choices.

    Authors: The manuscript deliberately uses one scene to supply recorded ground-truth data and occlusion cases that are absent from existing 360° collections. We will expand the experimental-setup section to state the scene count explicitly, report the exact view count, provide occlusion statistics, and detail the ground-truth recording procedure. This addresses the request for transparency while noting that the single-scene design inherently limits broad generalization statements. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a purely empirical benchmarking paper that introduces a new multi-object 3DGS scene with recorded ground truth and compares existing 2D inpainters plus integration strategies. No derivations, equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided abstract or description. The central claims rest on direct measurements from the new dataset, which is externally falsifiable and does not reduce to any input quantity by construction. This matches the default expectation for non-circular empirical studies.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Claims rest on the domain assumption that multi-view consistency can be meaningfully quantified by applying 2D inpainters independently per view and that the introduced dataset captures representative real-world occlusion challenges. No free parameters, invented entities, or additional axioms are stated in the abstract.

axioms (1)
  • domain assumption Multi-view consistency of inpainted 3DGS scenes can be evaluated by comparing rendered views against recorded ground truth after 2D inpainting.
    Implicit in the benchmarking setup described in the abstract.

pith-pipeline@v0.9.1-grok · 5707 in / 1352 out tokens · 29432 ms · 2026-06-28T22:49:52.871647+00:00 · methodology

discussion (0)

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