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REVIEW 3 major objections 5 minor 70 references

Defurnishing with X-Ray Vision: Joint Removal of Furniture from Panoramas and Mesh

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that a simplified defurnished mesh, rendered into depth and normal edge maps, is the right control signal for removing furniture from both panoramas and 3D meshes, producing sharper and more accurate empty rooms than…

desk verdict A genuinely useful, practical defurnishing pipeline built on an SDM-guided ControlNet, but the headline quantitative claims rest on an in-distribution random-mask proxy and need stronger real-scene validation. read the letter →

arxiv 2506.05338 v2 pith:ASHMRTJY submitted 2025-06-05 cs.CV

Alan Dolhasz , Chen Ma , Dave Gausebeck , Kevin Chen , Gregor Miller , Lucas Hayne , Gunnar Hovden , Azwad Sabik
show 2 more authors
Olaf Brandt Mira Slavcheva
This is my paper · ORCID
classification cs.CV
keywords defurnishing360-degreepanoramainpainting3DmesheditingControlNetStableDiffusiongeometricpriordigitaltwinsindoorsceneunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that furniture can be removed from scanned indoor spaces, in both the 3D mesh and the 360-degree panoramas, by first building a simplified defurnished mesh that acts as an X-ray of the room's structure. The mesh is rendered into depth and normal images, edges are extracted, and those edges guide a ControlNet-based Stable Diffusion inpainter to fill furniture regions with empty-room content. The inpainted panoramas then re-texture the mesh. The paper argues this beats radiance-field methods on sharpness and mesh accuracy, reporting a synthetic test where the simplified mesh error is 2.3 cm versus 24.1 cm for NeRFiller, and beats plain Stable Diffusion and off-the-shelf ControlNet on perceptual metrics. A reader should care because defurnishing is the enabling step for virtual staging and clean digital twins.

What carries the argument

The simplified defurnished mesh (SDM) is the central object: a planar-approximated version of the original mesh with furniture faces removed and holes closed by extending neighboring wall and floor planes, representing the room as if empty. Rendered depth and normal images from the SDM, run through a Canny edge detector, become the control signal for a fine-tuned Canny ControlNet inpainter. The fine-tuning uses 50,000 unfurnished panoramas with random composite masks, and the paper adds an FFTMax loss during super-resolution that penalizes predicted high-frequency energy only when it exceeds the target, suppressing artefacts while restoring texture detail.

What would settle it

Take one real furnished scene, physically remove the furniture and scan the empty room as ground truth, then run the pipeline on the furnished panoramas and measure masked-region error against the empty-room ground truth; if the advantage over vanilla Stable Diffusion shrinks or disappears, the random-mask fine-tuning is the weak link.

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Extended reading notes

Core claim

The central claim is that the geometry of the unfurnished room, not the pixel content, is the right control signal for furniture removal. The method obtains a simplified defurnished mesh (SDM) by removing furniture faces and filling holes with plane extensions, renders depth and normal maps from the SDM, extracts Canny edges from those maps, and feeds the edges as ControlNet conditioning to a Stable Diffusion inpainter. The geometric prior carries information hidden by furniture in any single panorama, so the inpaint follows true wall, floor, and ceiling edges rather than hallucinated content. The paper reports that this yields sharper panoramas and cleaner meshes than radiance-field inpainting, and better masked-region perceptual metrics than vanilla Stable Diffusion and off-the-shelf Canny ControlNet.

Load-bearing premise

The load-bearing premise is that random composite masks painted on unfurnished panoramas reproduce the statistics of real furniture removal, including long wardrobes and the shadows and reflections that the segmentation masks do not cover; the quantitative evaluations are built on this proxy task.

Editorial extensions

If this is right

  • If the claim holds, defurnished digital twins can be produced at native panorama resolution with explicit clean meshes, avoiding the blur and volumetric blobs left by radiance-field methods.
  • For downstream tasks requiring metric accuracy, the SDM provides an order-of-magnitude better geometry than radiance-field inpainting on the synthetic test: 2.3 cm versus 24.1 cm root mean squared error.
  • Because the control signal is geometric rather than semantic, the same mesh-guided inpainting can be repurposed for related tasks such as clutter removal, as demonstrated on a ScanNet scene.
  • The pipeline runs in roughly 10 minutes for a 30-panorama scene on a single A10G GPU, making it practical for real-estate-scale processing.
  • The method remains vulnerable to hallucination, ignored control signals, spurious shadows, and view inconsistency in occluded regions, as the paper's failure cases show.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the authors leave implicit is that the random-composite-mask fine-tuning is a proxy for real furniture removal statistics; a testable next step is to collect real mask-shadow pairs and fine-tune on those instead.
  • Because the SDM control is purely geometric, the same pipeline could plausibly extend to other structure-guided edits, such as removing built-in clutter or altering room layout, without retraining the inpainter beyond the control channel.
  • A natural hybrid not tested here would feed the SDM edge maps into a multi-view transformer inpainter to enforce cross-view consistency, though current multi-view transformers operate at lower resolution.
  • The 2.3 cm versus 24.1 cm geometry comparison is on synthetic data with inserted Objaverse objects; reproducing the same protocol on real furnished scans would show whether the gap persists outside synthetic settings.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript presents "Defurnishing with X-Ray Vision," a pipeline that jointly removes furniture from textured indoor meshes and their associated 360° panoramas. The method first segments furniture in panoramas with a semantic segmentation model, projects the masks onto the mesh, simplifies the mesh, removes furniture faces, and closes holes to obtain a simplified defurnished mesh (SDM). Depth and normal images rendered from the SDM are converted to Canny edge maps, which serve as control signals for ControlNet-based inpainting of the panoramas; the ControlNet is fine-tuned on 50,000 unfurnished panoramas with random composite masks. The inpainted panoramas are then super-resolved, blended, and used to texture the SDM. The paper reports ablations against base Stable Diffusion inpainting and an off-the-shelf Canny ControlNet, qualitative comparisons to NeRFiller and Wei et al., and a synthetic geometry experiment showing lower RMSE than NeRFiller.

Significance. If the empirical claims hold, the pipeline is practically valuable: it targets a real application, produces high-resolution defurnished panoramas and clean meshes at a modest runtime, and introduces a geometric prior that does not rely on semantic labels at inference time. The paper is also transparent about failure cases and includes a synthetic geometry test. The core idea of using SDM-rendered edges as global structure guidance for 2D inpainting is sensible and potentially useful. However, the quantitative validation currently centers on a proxy task that closely matches the training distribution, and the synthetic geometry test does not exercise the inpainting/control component; the significance of the X-ray control over standard inpainting is therefore not yet established.

major comments (3)
  1. [Section 4.1, Table 1] The ablation in Section 4.1 is computed on 700 unfurnished panoramas with random masks and SDM-derived Canny controls, which is the same task used to fine-tune the ControlNet in Section 3.4 (50,000 unfurnished panoramas, composite random-circle masks). The comparison therefore measures in-distribution performance on the synthetic training task rather than defurnishing of real furnished scenes, where furniture masks are elongated and correlated and where shadows and reflections are not covered by the masks. The observed gains over base SD and the off-the-shelf Canny ControlNet may reflect adaptation to equirectangular image statistics and to the SDM edge style rather than a generalizable advantage on real furniture removal. Furthermore, the paper states that all metrics are calculated before super-resolution or blending are applied, so Table 1 does not measure the final output quality. Please add an evaluation on real furnished scenes, ideally with manually refined masks and metrics on the final post-processed results, before claiming that the X-ray control improves defurnished asset quality.
  2. [Section 4.2 and Supplementary Section 8] The synthetic geometry result, 2.3 cm RMSE for the SDM versus 24.1 cm for NeRFiller, compares the SDM itself against a ground-truth unfurnished mesh in a procedurally furnished scene. Because the SDM is obtained by planar simplification and plane extension from the same type of mesh representation, this largely validates the mesh-processing construction on piecewise-planar scenes; it does not measure the effect of the ControlNet inpainting or the final textured asset. A reader cannot infer from this experiment that the X-ray control improves the output on real data. Please report geometry error for the full pipeline on real furnished scenes, or state explicitly in the main text that this number is for the SDM only and not for the final textured output.
  3. [Section 3.1, Figure 5, Figure 7c] The segmentation masks explicitly exclude shadows and reflections cast by furniture, and Figure 7c lists 'spurious shadows' as a failure mode. Yet the comparison to NeRFiller in Figure 5 is qualitative and claims that our method is 'trained to be robust' to light reflections and shadows. No quantitative evidence on real scenes supports this robustness, and the synthetic image metrics in Table 2 use procedurally inserted furniture with approximate shadows rather than the true shadow and reflection statistics of real panoramas. Please provide a real-scene evaluation that isolates shadow and reflection handling, or temper the robustness claim to match the available evidence.
minor comments (5)
  1. [Section 3.4] The composite mask generation ranges (number of circles, radii, center locations) are described only as 'predefined ranges'; please specify the actual values for reproducibility.
  2. [Supplementary Material, Figure 10 caption] The word 'perspetive' should be 'perspective'.
  3. [Supplementary Material, Section 8] There are typos in the final paragraph: 'on avergae' should be 'on average' and 'magniture' should be 'magnitude'.
  4. [Section 3.5, Eq. (1)] The FFTMax loss divides by X_T(x), which can be zero for frequency components that are absent in the target image; please define a guard or clarify how this case is handled.
  5. [Section 3.1, Reference [60]] The citation for ADE20K points to a paper on cooperative semantic segmentation; the standard ADE20K dataset reference would be more appropriate for the claim that the training data is 'similar to ADE20K'.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the in-distribution random-mask ablation is a proxy limitation, not a derivation, and the real-scene and synthetic comparisons provide independent support.

full rationale

The paper's central steps are constructive, not definitional: the SDM is produced from the furnished mesh via segmentation, face removal, and plane extension, and its claimed accuracy is checked against ground-truth unfurnished meshes in a synthetic insertion experiment (2.3 cm vs 24.1 cm RMSE). The image-side contribution is checked against external baselines, including off-the-shelf Canny ControlNet and NeRFiller, on Matterport3D and ScanNet. The Table 1 ablation is indeed an in-domain proxy: the model was fine-tuned on 50,000 unfurnished panoramas with random composite masks and SDM-derived Canny edges, and the 700-image ablation uses the same random-mask protocol and the same SDM Canny controls. This is a limitation of that specific experiment, because it does not test real, elongated furniture masks or unmasked shadows and reflections, but the paper labels it as a simulated-furniture-removal ablation and does not derive the real-scene claim from it alone. The self-citation [42] supplies the base SD weights and blending; it is a prior published pipeline component, and the SDM plus ControlNet contribution is independently evaluated, so the self-citation is not load-bearing. The FFTMax loss is asserted without an ablation, but a missing evaluation is not circularity. Overall, there is no significant circularity: the strongest claims rest on separate real-scene and synthetic benchmarks.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The free parameters are mostly unspecified hand choices. The axioms are standard domain assumptions for a learned pipeline; none are parameter-free derivations. No new physical entities are introduced; the SDM is a data structure, and FFTMax is a loss function, not an invented entity in the sense of a new force or conserved quantity.

free parameters (5)
  • Furniture class ontology = Predefined list including chairs, tables, sofas, decorations, humans, animals
    Selection of which semantic classes count as removable furniture is hand-chosen and affects the masks that drive both mesh removal and inpainting (Section 3.1).
  • Composite mask generation ranges = Not specified
    Number of circles, radii, and center locations are sampled from predefined ranges that are never given; these generate the fine-tuning targets (Section 3.4).
  • SDM simplification parameters = Not specified
    Planar approximation scale, projection distance weighting, plane extension, and doorway-preservation heuristics are described qualitatively with no numeric settings (Section 3.2).
  • Canny edge thresholds = Not specified
    Canny parameters for depth and normal edge extraction are not reported (Section 3.3).
  • FFTMax and LoG loss weights = Not specified
    The combination coefficients for the contrast loss and FFTMax are not given, and no ablation shows their effect (Section 3.5).
assumptions (5)
  • domain assumption Semantic segmentation is accurate enough to delineate furniture from structure.
    The whole pipeline depends on the furniture masks produced by the off-the-shelf segmentation model; mask errors propagate to mesh removal and Canny controls (Section 3.1).
  • ad hoc to paper Random circular masks on unfurnished panoramas approximate real furniture occlusion during training.
    Section 3.4 states this mimics removing arbitrary objects; if real furniture masks have different statistics, such as long wardrobes, shadows, and reflections, the fine-tuned model may not transfer.
  • domain assumption The SDM preserves the true geometric structure after planar simplification and hole filling.
    Section 3.2 relies on planar surfaces approximating the scene; the paper's own limitation section notes that complex meshes can yield misleading Canny edges.
  • domain assumption Depth and normal Canny edges from the SDM provide sufficient global geometric guidance for the inpainter.
    Section 3.3 introduces this control; the Limitations section admits the signal can be insufficient when regions are occluded in a view and do not appear elsewhere.
  • domain assumption Training on unfurnished rooms with synthetic masks transfers to real furnished scenes.
    The quantitative evaluation is on unfurnished images; the real-scene results are only qualitative and could differ in lighting, clutter, and camera noise.

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Pith. "Pith review of Defurnishing with X-Ray Vision: Joint Removal of Furniture from Panoramas and Mesh." pith.science (2026). https://pith.science/paper/ASHMRTJY

@misc{pith2026250605338,
  author       = {Pith},
  title        = {Pith review of: Defurnishing with X-Ray Vision: Joint Removal of Furniture from Panoramas and Mesh},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ASHMRTJY}},
  note         = {Machine review of arXiv:2506.05338}
}
read the original abstract

We present a pipeline for generating defurnished replicas of indoor spaces represented as textured meshes and corresponding multi-view panoramic images. To achieve this, we first segment and remove furniture from the mesh representation, extend planes, and fill holes, obtaining a simplified defurnished mesh (SDM). This SDM acts as an ``X-ray'' of the scene's underlying structure, guiding the defurnishing process. We extract Canny edges from depth and normal images rendered from the SDM. We then use these as a guide to remove the furniture from panorama images via ControlNet inpainting. This control signal ensures the availability of global geometric information that may be hidden from a particular panoramic view by the furniture being removed. The inpainted panoramas are used to texture the mesh. We show that our approach produces higher quality assets than methods that rely on neural radiance fields, which tend to produce blurry low-resolution images, or RGB-D inpainting, which is highly susceptible to hallucinations.

Figures

Figures reproduced from arXiv: 2506.05338 by the authors.

Figure 1
Figure 1. Furniture removal based on simplified defurnished mesh (SDM). We produce an SDM by removing furniture faces and closing holes in the input mesh. Then we render the SDM into depth and normal images, from which we extract Canny edges to use as structural guidance in ControlNet (CN) inpainting of the corresponding panorama images (right). Inpainting only with Stable Diffusion (SD) leads to warped lines in the output, s… view at source ↗
Figure 2
Figure 2. Defurnishing pipeline overview. Panoramic image segmentation guides simplification and defurnishing of an input textured mesh. Canny edges from the simplified mesh guide CN-based image defurnishing for final textured mesh reconstruction. 3. Method This section details the defurnishing pipeline designed for 360° panorama images and a corresponding 3D textured mesh, reconstructed from these input images. The pipeline … view at source ↗
Figure 3
Figure 3. Hole filling comparison between MeshLab’s screened Poisson hole filling [7] and our proposed simplified defurnished mesh method. Poisson re-meshing tends to warp surfaces between floors and walls, while they do not interfere in our SDM. ing any gaps using plane extension. This technique ex￾ploits the planar approximation of the mesh to seamlessly extend neighbouring planes and close the gaps left by the removed furn… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Ablation of control method used to guide defurnishing. Plain SD inpainting often results in warped, unrealistic geometry, such as the wall-floor fusion on the first two rows. The use of Canny edge guided CN makes the inpainting process follow the underlying structure, …
Figure 5
Figure 5. Figure 5: Defurnishing comparison with a radiance field based method Nerfiller [56] on a small (180 input images, top) and a big (366 images, bottom) space from Matterport3D [5]. Light reflections and shadows on walls mislead Nerfiller to generate objects that can cause these ef…
Figure 7
Figure 7. Figure 7: Failure case examples. (a) Ignored control signal: The kitchen island is largely removed despite the existence of corre￾sponding Canny edges. (b) Hallucination of a radiator after furni￾ture removal. (c) Spurious shadows: The shadow of a sofa is not fully removed. Inpu…
Figure 6
Figure 6. Figure 6: Declutter comparison. We modify our method for de￾cluttering instead of full defurnish. We achieve cleaner, smoother surfaces than the RGB-D inpainting method of Wei et al. [58]. Mesh clutter removal To the best of our knowledge, the only other method that deals with a…
Figure 8
Figure 8. Figure 8: Pairwise comparisons of perspective renders of furnished inputs and results defurnished using our pipeline. This projection highlights some remaining issues with straight wall/floor/ceiling edges, which do not always get resolved, even when using Canny Con￾trolNet. 2 …
Figure 9
Figure 9. Figure 9: Hole filling comparison between MeshLab’s screened Poisson hole filling [7] and our proposed simplified defurnished mesh method. Poisson re-meshing tends to warp surfaces between floors and walls, while they do not interfere in our SDM. only leading to amplification of…
Figure 10
Figure 10. Figure 10: Ablation of control method used to guide defurnishing (shown as perspective crops from [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Failure case examples. Ignored control signal: The kitchen island in a) is largely removed in b), despite the existence of corresponding Canny edges. Hallucination: after removing the furniture in c) a radiator is hallucinated in d). Spurious shadows: the shadow of th…
Figure 12
Figure 12. Figure 12: Radiance field initialization comparison. Posed RGB-only NeRF (nerfacto and nerfacto-nerfiller) exhibits more floater artifacts than posed RGB-D NeRF (depth-nerfacto), while posed RGB-only 3D Gaussian splatting (splatfacto) is cleanest. 6 [PITH_FULL_IMAGE:figures/ful…
Figure 13
Figure 13. Figure 13: Defurnishing comparison with a radiance field based method Nerfiller [56]. 7 [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: Inpainted frames and mesh extracted via Poisson surface reconstruction on Nerfiller’s inpainted model on the ScanNet scene from Section 4.2 [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: Instruct-NeRF2NeRF [15] experiments on furniture removal. The prompt used was remove all furniture from this space. The modified scene gets progressively blurrier over time, due to amplification of floaters in the initial NeRF. 9 [PITH_FULL_IMAGE:figures/full_fig_p02…
Figure 16
Figure 16. Figure 16: Instruct-GS2GS [49] experiments on furniture removal and modification. The prompt used for each experiment is shown in the captions above. While better than Instruct-NeRF2NeRF, Instruct-GS2GS is not sufficiently spatially accurate for our purposes, as evident from the…

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