SimRecon：从真实视频重建仿真就绪的组合式场景
SimRecon: SimReady Compositional Scene Reconstruction from Real Videos
把真实视频重建为可交互的仿真就绪组合式资产：先用场景级语义重建得到物体级分割与位姿，再对每个物体做单物体补全生成，最后用场景图在仿真器中从零组装；其中主动视角优化在 3D 空间搜索最佳投影视图作为生成条件，是提升生成资产视觉保真度的可复用手段。
原文依据 we introduce Active Viewpoint Optimization, which actively searches in 3D space to acquire optimal projected images as conditions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论组合式场景重建旨在从真实世界视频中构建以物体为中心的表示，而非整体场景，这种表示天然适用于仿真与交互。传统的组合式重建方法主要关注视觉外观，对真实场景的泛化能力有限。本文提出 SimRecon，一个实现「感知—生成—仿真」流程的框架，面向杂乱场景重建：首先从视频输入进行场景级语义重建，然后执行单物体生成，最后在仿真器中组装这些资产。然而，简单地将这三个阶段拼接会导致生成资产的视觉不保真以及最终场景的物理不合理性，这一问题在复杂场景中尤为严重。为此，我们进一步在三个阶段之间提出两个桥接模块以解决该问题。具体而言，对于从感知到生成的过渡（该过渡对视觉保真度至关重要），我们引入主动视角优化，它在三维空间中主动搜索以获取最优投影图像，作为单物体补全的条件。此外，对于从生成到仿真的过渡（该过渡对物理合理性至关重要），我们提出场景图合成器，它引导在三维仿真器中从零开始构建，以镜像真实世界原生的构造性原则。在 ScanNet 数据集上的大量实验验证了本方法优于此前最优方法。
英文原文摘要 1603 字符
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.