Compositional scene reconstruction seeks to create object-centric representations rather than holistic scenes from real-world videos, which is natively applicable for simulation and interaction. Conventional compositional reconstruction approaches primarily emphasize on visual appearance and show limited generalization ability to real-world scenarios. In this paper, we propose SimRecon, a framework that realizes a "Perception-Generation-Simulation" pipeline towards cluttered scene reconstruction, which first conducts scene-level semantic reconstruction from video input, then performs single-object generation, and finally assembles these assets in the simulator. However, naively combining these three stages leads to visual infidelity of generated assets and physical implausibility of the final scene, a problem particularly severe for complex scenes. Thus, we further propose two bridging modules between the three stages to address this problem. To be specific, for the transition from Perception to Generation, critical for visual fidelity, we introduce Active Viewpoint Optimization, which actively searches in 3D space to acquire optimal projected images as conditions for single-object completion. Moreover, for the transition from Generation to Simulation, essential for physical plausibility, we propose a Scene Graph Synthesizer, which guides the construction from scratch in 3D simulators, mirroring the native, constructive principle of the real world. Extensive experiments on the ScanNet dataset validate our method's superior performance over previous state-of-the-art approaches.

翻译：组合式场景重建旨在从真实世界视频中创建以对象为中心而非整体场景的表征，这天然适用于仿真与交互。传统组合式重建方法主要侧重于视觉外观，对真实场景的泛化能力有限。本文提出SimRecon框架，实现面向杂乱场景重建的“感知-生成-仿真”流程：首先从视频输入进行场景级语义重建，随后执行单对象生成，最终在仿真器中组合这些资产。然而，简单串联这三个阶段会导致生成资产的视觉失真与最终场景的物理不合理性，该问题在复杂场景中尤为突出。为此，我们进一步提出连接三个阶段的桥接模块以解决此问题。具体而言，针对影响视觉保真度的感知到生成过渡，我们引入主动视角优化，通过在三维空间中主动搜索以获取最优投影图像作为单对象补全的条件。此外，针对决定物理合理性的生成到仿真过渡，我们提出场景图合成器，其遵循现实世界固有的构造性原理，指导三维仿真器中的场景从零构建。在ScanNet数据集上的大量实验验证了本方法相较于现有先进方案的优越性能。