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arxiv: 2605.16807 · v1 · pith:X43KUQV7new · submitted 2026-05-16 · 💻 cs.CV

DecoRec: Decomposed 3D Scene Reconstruction from Single-View Images via Object-Level Diffusion

Yuhan Ping , Yuan Liu , Xiaoxiao Long , Peng Wang , Junhui Hou , Jianyi Zheng , Jia Pan , Xin Li
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Cheng Lin
This is my paper

Pith reviewed 2026-05-19 21:06 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D reconstructionsingle viewdiffusion modelsobject decompositionscene meshnovel view synthesisdifferentiable rendering
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The pith

DecoRec reconstructs 3D scenes from single-view images by diffusing objects individually then refining their merge.

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

DecoRec aims to create accurate 3D scene meshes from a single 2D photo by breaking the scene into separate objects. Each object is reconstructed using diffusion models trained for single-view object reconstruction. These pieces are then assembled and refined with differentiable rendering and diffusion guidance to fix geometry and appearance issues. This matters because direct scene reconstruction often fails due to poor datasets and coarse methods, while this object-wise approach promises better fidelity for tasks like designing virtual rooms.

Core claim

The central claim is that by reconstructing each object in the scene separately with diffusion-based single-view methods and then merging them through a refinement pipeline that uses differentiable rendering and diffusion guidance, high-quality 3D scene reconstruction and novel view synthesis become possible from just one image.

What carries the argument

The decomposition of the scene into individual objects reconstructed via diffusion models, followed by a refinement pipeline for merging using differentiable rendering and diffusion-guided adjustments.

Load-bearing premise

That the refinement pipeline can reliably resolve any geometric or appearance inconsistencies introduced when merging the separately reconstructed objects.

What would settle it

Observing a case where individually accurate object reconstructions lead to a merged scene with uncorrectable errors in surface alignment or texture continuity despite the refinement steps.

Figures

Figures reproduced from arXiv: 2605.16807 by Cheng Lin, Jianyi Zheng, Jia Pan, Junhui Hou, Peng Wang, Xiaoxiao Long, Xin Li, Yuan Liu, Yuhan Ping.

Figure 1
Figure 1. Figure 1: Given a single viewpoint photo of an indoor scene or an image of [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Refinement comparison. Single-view reconstruction method cannot [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our pipeline. Given an input image and object masks, our method first performs a coarse decomposition and reconstruction for both [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Background reconstruction. We reconstruct a complete background [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison with other methods on the real-world dataset. From top to bottom, we show one novel-view rendering and the corresponding [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison with other methods on the synthetic dataset. From top to bottom, we show two novel views and the corresponding 3D [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison with Gen3DSR on stylized inputs. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ablation studies on different loss settings. Without any designed losses, it will cause issues like texture blurring, structure distortion, and black borders. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Flexibility of scene editing. Our method enables decomposed 3D lifting of 2D images and thus, users can perform different editing operations [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Additional results with implementation of Hunyuan3D-2 and [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: More comparisons between coarse and refinement stages. [PITH_FULL_IMAGE:figures/full_fig_p008_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Result gallery of qualitative comparison with other methods. [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Result gallery of qualitative comparison with other methods. [PITH_FULL_IMAGE:figures/full_fig_p009_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Result gallery of qualitative comparison with other methods. [PITH_FULL_IMAGE:figures/full_fig_p010_15.png] view at source ↗
Figure 18
Figure 18. Figure 18: Comparisons of background reconstruction results. The middle and [PITH_FULL_IMAGE:figures/full_fig_p010_18.png] view at source ↗
Figure 16
Figure 16. Figure 16: Result of our method on more types of scenes, including kitchen, [PITH_FULL_IMAGE:figures/full_fig_p010_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Comparison with Gen3DSR by giving the same mask as our method. [PITH_FULL_IMAGE:figures/full_fig_p010_17.png] view at source ↗
read the original abstract

In this paper, we introduce \textit{DecoRec}, a novel system designed to elevate single-view 2D images to a decomposed 3D scene mesh. Current methods for single-view scene reconstruction typically rely on object retrieval or the regression of coarse 3D voxels or surfaces, leading to inaccuracies in capturing the appearance and geometry of the input image. The lack of high-quality large-scale scene-level datasets further complicates direct 3D scene generation from single-view images. To achieve high-quality 3D scene generation from a single-view image, DecoRec takes advantage of recent diffusion-based single-view object reconstruction methods to reconstruct individual objects separately. Subsequently, a refinement pipeline is proposed to effectively merge these reconstructed objects, enhancing appearance and geometry through a differentiable rendering technique and diffusion-guided refinement. Our results demonstrate that DecoRec facilitates high-quality single-view scene reconstruction in both geometry and novel synthesis, offering significant benefits for downstream applications like room interior design.

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 paper introduces DecoRec, a system for single-view 3D scene reconstruction that decomposes the input into individual objects, reconstructs each using existing diffusion-based single-view object methods, estimates poses to place them, and then applies a refinement pipeline with differentiable rendering and diffusion guidance to produce a coherent scene mesh. The central claim is that this yields high-quality geometry and novel-view synthesis superior to direct scene-level approaches, without requiring large scene datasets.

Significance. If validated, the decomposed approach would be a useful contribution to single-view scene reconstruction by leveraging strong object-level priors and a post-merging refinement stage. Credit is given for the modular design that reuses external diffusion models and for proposing an independent merging/refinement stage rather than end-to-end scene generation. The emphasis on both geometry and appearance consistency via differentiable rendering is a reasonable direction for addressing under-constrained single-view problems.

major comments (2)
  1. [Results / Experiments] The abstract states that results demonstrate high-quality geometry and novel-view synthesis, yet the supplied text contains no quantitative metrics (e.g., Chamfer distance, IoU, or PSNR), ablation studies, or error analysis. This is load-bearing for the central claim; the results section must include controlled comparisons to baselines and targeted failure-case analysis (occlusions, contacts) to substantiate superiority.
  2. [Method / Refinement pipeline] The refinement pipeline (described after object reconstruction) relies on differentiable rendering plus diffusion guidance to resolve inter-object inconsistencies. Without explicit 3D consistency terms (e.g., contact or penetration losses) or ablation on scenes with touching/occluding objects, it is unclear whether residual geometric drift or appearance seams are reliably eliminated; this directly affects the coherence guarantee.
minor comments (2)
  1. [Method] Notation for the merging stage (pose estimation, rigid placement) should be formalized with equations or a clear algorithmic outline to improve reproducibility.
  2. [Abstract] The abstract would benefit from naming the specific object-reconstruction diffusion models used and the scene categories evaluated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below and describe the changes planned for the revised manuscript.

read point-by-point responses

  1. Referee: [Results / Experiments] The abstract states that results demonstrate high-quality geometry and novel-view synthesis, yet the supplied text contains no quantitative metrics (e.g., Chamfer distance, IoU, or PSNR), ablation studies, or error analysis. This is load-bearing for the central claim; the results section must include controlled comparisons to baselines and targeted failure-case analysis (occlusions, contacts) to substantiate superiority.

    Authors: We agree that the absence of quantitative metrics weakens the central claim. The current manuscript emphasizes qualitative results partly because of the scarcity of standardized large-scale scene benchmarks for single-view reconstruction. In the revision we will add controlled quantitative comparisons using Chamfer distance and IoU for geometry as well as PSNR and SSIM for novel-view synthesis, together with ablations and a dedicated failure-case analysis focused on occlusions and object contacts. revision: yes

  2. Referee: [Method / Refinement pipeline] The refinement pipeline (described after object reconstruction) relies on differentiable rendering plus diffusion guidance to resolve inter-object inconsistencies. Without explicit 3D consistency terms (e.g., contact or penetration losses) or ablation on scenes with touching/occluding objects, it is unclear whether residual geometric drift or appearance seams are reliably eliminated; this directly affects the coherence guarantee.

    Authors: The refinement stage optimizes the merged scene via differentiable rendering under diffusion guidance from the input and generated novel views; the strong object-level priors embedded in the diffusion model are intended to reduce inter-object drift and seams without hand-crafted contact losses. We acknowledge that this mechanism would be clearer with targeted evidence. In the revision we will therefore include ablations on scenes containing touching and occluding objects, reporting both qualitative coherence and quantitative consistency metrics. revision: partial

Circularity Check

0 steps flagged

No significant circularity; method builds on external priors with independent refinement stage

full rationale

The paper describes a pipeline that applies existing diffusion-based single-view object reconstruction methods to individual objects, followed by a separate refinement stage using differentiable rendering and diffusion guidance to merge them. No equations, fitted parameters renamed as predictions, or self-citation chains that reduce the central claim to its own inputs are present in the abstract or described approach. The derivation is self-contained against external benchmarks (prior diffusion models) and does not rely on self-definitional steps or uniqueness theorems imported from the authors' prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract introduces no explicit free parameters, mathematical axioms, or newly postulated entities; the approach relies on existing diffusion models for single objects and standard differentiable rendering without stating additional unproven assumptions beyond the effectiveness of the proposed merging stage.

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discussion (0)

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