We propose LiteReality, a novel pipeline that converts RGB-D scans of indoor environments into compact, realistic, and interactive 3D virtual replicas. LiteReality not only reconstructs scenes that visually resemble reality but also supports key features essential for graphics pipelines -- such as object individuality, articulation, high-quality physically based rendering materials, and physically based interaction. At its core, LiteReality first performs scene understanding and parses the results into a coherent 3D layout and objects with the help of a structured scene graph. It then reconstructs the scene by retrieving the most visually similar 3D artist-crafted models from a curated asset database. Next, the Material Painting module enhances realism by recovering high-quality, spatially varying materials. Finally, the reconstructed scene is integrated into a simulation engine with basic physical properties to enable interactive behavior. The resulting scenes are compact, editable, and fully compatible with standard graphics pipelines, making them suitable for applications in AR/VR, gaming, robotics, and digital twins. In addition, LiteReality introduces a training-free object retrieval module that achieves state-of-the-art similarity performance on the Scan2CAD benchmark, along with a robust material painting module capable of transferring appearances from images of any style to 3D assets -- even under severe misalignment, occlusion, and poor lighting. We demonstrate the effectiveness of LiteReality on both real-life scans and public datasets. Project page: https://litereality.github.io; Video: https://www.youtube.com/watch?v=ecK9m3LXg2c

翻译：我们提出了LiteReality，一种新颖的流程，可将室内环境的RGB-D扫描转换为紧凑、逼真且可交互的3D虚拟副本。LiteReality不仅重建出视觉上与现实相似的场景，还支持图形管线所需的关键特性——例如物体个体性、关节连接、高质量的基于物理的渲染材质以及基于物理的交互。其核心在于，LiteReality首先进行场景理解，并借助结构化场景图将解析结果整合为连贯的3D布局和物体。随后，通过从精选的资产数据库中检索视觉上最相似的3D艺术家制作模型来重建场景。接着，材质绘制模块通过恢复高质量、空间变化的材质来增强真实感。最后，重建的场景被集成到具有基本物理属性的仿真引擎中，以实现交互行为。生成的场景紧凑、可编辑，且完全兼容标准图形管线，使其适用于AR/VR、游戏、机器人和数字孪生等应用。此外，LiteReality引入了一个无需训练的对象检索模块，该模块在Scan2CAD基准测试中实现了最先进的相似性性能，以及一个鲁棒的材质绘制模块，能够将任意风格的图像外观迁移到3D资产上——即使在严重错位、遮挡和光照不佳的条件下也能实现。我们在真实扫描和公共数据集上验证了LiteReality的有效性。项目页面：https://litereality.github.io；视频：https://www.youtube.com/watch?v=ecK9m3LXg2c