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ReScene framework reconstructs 3D indoor scenes with improved accuracy · arXiv paper

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Researchers have developed ReScene, a new framework designed to construct simulation-ready 3D indoor scenes from multi-view captures. This method addresses limitations in existing approaches by focusing on cross-view relation fusion and physically plausible scene assembly, rather than just single-object reconstruction. ReScene utilizes a HierView component for prioritizing reconstruction views and Relation-Aware Assembly to integrate multi-frame predictions with geometric priors, resulting in a confidence-weighted scene graph. The framework achieves state-of-the-art performance on ScanNet scenes, significantly reducing Chamfer Distance and LPIPS metrics while operating faster than previous multi-view techniques. Additionally, ReScene enables the creation of a new embodied visual question answering dataset, where a fine-tuned Qwen-VL model demonstrates strong spatial reasoning capabilities. AI

IMPACT Enhances the creation of realistic 3D environments for embodied AI research and applications.

RANK_REASON The cluster contains an arXiv paper detailing a new framework for 3D scene reconstruction.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

ReScene framework reconstructs 3D indoor scenes with improved accuracy · arXiv paper

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COVERAGE [3]

  1. arXiv cs.CV TIER_1 English(EN) · Guoqiu Li, Jin Song, Yiyun Fei ·

    HomeDiffusion: Zero-Shot Object Customization with Multi-View Representation Learning for Indoor Scenes

    arXiv:2606.29828v1 Announce Type: new Abstract: Recently, zero-shot object customization generation methods have rapidly developed and shown tremendous potential for applications. For instance, in the e-commerce domain, consumers can observe the visual effect of furniture placed …

  2. arXiv cs.CV TIER_1 English(EN) · Haoran Xu, Lechao Zhang, Daoguo Dong, Yan Gao, Xin Tan ·

    ReScene: Structured Indoor Scene Reconstruction from Multi-View Captures

    arXiv:2606.28060v1 Announce Type: new Abstract: Constructing simulation-ready 3D scenes from multi-view captures is a key bottleneck for Embodied Artificial Intelligence, as downstream tasks require object-level structure, explicit inter-object relations, and physical plausibilit…

  3. arXiv cs.CV TIER_1 English(EN) · Xin Tan ·

    ReScene: Structured Indoor Scene Reconstruction from Multi-View Captures

    Constructing simulation-ready 3D scenes from multi-view captures is a key bottleneck for Embodied Artificial Intelligence, as downstream tasks require object-level structure, explicit inter-object relations, and physical plausibility. Existing approaches either rely on specialize…

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