通过分解物体重建3D场景，让单张图像生成更逼真的三维空间。
DecoRec: Decomposed 3D Scene Reconstruction from Single-View Images via Object-Level Diffusion
- 分步重建每个物体，再融合成完整场景
- 结合扩散模型与可微渲染提升几何和外观质量
- 适合室内设计等需要高保真3D重建的场景
本文提出DecoRec，一种从单张2D图像生成分解式3D场景网格的新系统。现有单视图场景重建方法多依赖物体检索或粗粒度体素/表面回归，难以准确还原输入图像的外观与几何。缺乏高质量大规模场景级数据集也限制了直接从单视图生成3D场景。DecoRec利用基于扩散的单视图物体重建方法，分别重建各个物体，随后通过可微渲染与扩散引导的精炼流程，有效融合物体，显著提升外观与几何精度。实验表明，DecoRec在几何重建与新视角合成方面均达到高质量效果，对房间室内设计等下游应用具有重要价值。
原文摘要 · Abstract (English)
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.
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