用手机实时重建高质纹理房间模型，支持交互式扫描与智能贴图。
RoomRecon: High-Quality Textured Room Layout Reconstruction on Mobile Devices
- 分两阶段贴图：结合AR引导拍摄与生成式AI提升细节质量。
- 仅建模墙、地、顶等固定结构，模型可快速定制。
- 在手机上实现实时处理，贴图效果和速度优于现有方法。
RGB-D传感器与先进3D重建技术推动了室内空间的数字化，提升了增强现实（AR）、虚拟现实（VR）和扩展现实（XR）的应用体验。然而，当前技术仍存在无法感知细微场景变化、缺乏语义理解以及纹理渲染质量不足等问题，影响了虚拟场景的真实感，制约了在室内设计与房地产等领域的应用。为此，我们提出RoomRecon，一种面向移动设备的交互式实时扫描与贴图管道。该方法采用两阶段纹理生成流程，融合AR引导图像采集与生成式AI模型，显著提升纹理质量并更真实还原室内空间。我们聚焦于墙、地板、天花板等永久性结构，使3D模型更易定制。在多种室内场景中进行实验，定量结果与用户评估均表明，RoomRecon在纹理质量与本地计算效率方面优于当前最优方法。
原文摘要 · Abstract (English)
Widespread RGB-Depth (RGB-D) sensors and advanced 3D reconstruction technologies facilitate the capture of indoor spaces, improving the fields of augmented reality (AR), virtual reality (VR), and extended reality (XR). Nevertheless, current technologies still face limitations, such as the inability to reflect minor scene changes without a complete recapture, the lack of semantic scene understanding, and various texturing challenges that affect the 3D model's visual quality. These issues affect the realism required for VR experiences and other applications such as in interior design and real estate. To address these challenges, we introduce RoomRecon, an interactive, real-time scanning and texturing pipeline for 3D room models. We propose a two-phase texturing pipeline that integrates AR-guided image capturing for texturing and generative AI models to improve texturing quality and provide better replicas of indoor spaces. Moreover, we suggest focusing only on permanent room elements such as walls, floors, and ceilings, to allow for easily customizable 3D models. We conduct experiments in a variety of indoor spaces to assess the texturing quality and speed of our method. The quantitative results and user study demonstrate that RoomRecon surpasses state-of-the-art methods in terms of texturing quality and on-device computation time.
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