$\phi$-Scene: 物理驱动的图像到3D场景重建
$ϕ$-Scene: Physically Grounded Image-to-3D Scene Reconstruction
AI总结：
提出$\phi$-Scene方法，将单图像3D场景重建视为拓扑驱动的物理组装过程，通过SDF优化和刚体仿真解决穿透与不稳定接触问题，在3D-Front数据集上取得最优性能。
摘要：
从单张图像重建组合3D场景是3D世界建模中的基本挑战。最近的方法可以恢复高保真、完整的3D对象并预测合理的场景布局，但大多数仍将场景重建主要视为视觉和几何预测问题。因此，它们的输出可能包含漂浮物体、相互穿透或不稳定接触的伪影，限制了其在仿真、机器人和交互环境中的物理有效性和下游可用性。我们提出$\phi$-Scene，一种物理驱动的开放词汇组合图像到3D场景重建方法。关键前提是重建场景不应仅被视为一组具有预测姿态的对象，而应视为一个稳定的物理系统。因此，$\phi$-Scene将重建公式化为拓扑驱动的物理组装：它推断对象如何相互支撑，据此排序，并逐步将每个对象与其已稳定的支撑上下文稳定下来。对于按拓扑顺序排列的每个对象，基于SDF的优化首先解决与预稳定支撑上下文的穿透，然后刚体仿真在真实物理约束下将对象稳定到稳定的接触配置。在3D-Front上的实验表明，$\phi$-Scene在域外方法中实现了最强的整体性能，并且与域内基线保持高度竞争力。人类和VLM评估进一步显示$\phi$-Scene在视觉质量、参考对齐和物理合理性方面具有强烈偏好。最后，涵盖静态接触质量和动态稳定性的专用物理合理性指标表明，$\phi$-Scene显著减少了穿透伪影，同时产生更低的仿真后漂移，表明更稳定和物理合理的3D场景。
英文摘要：
Recent image-to-3D scene methods recover high-fidelity 3D objects with plausible arrangements, but often leave floatings and interpenetrations that limit physical validity and downstream use in interactive environments. We present $ϕ$-Scene, a physically grounded approach for open-vocabulary and compositional image-to-3D scene reconstruction that treats a scene not merely as a set of objects with predicted poses, but as a globally stable physical system. $ϕ$-Scene formulates reconstruction as topology-driven physical assembly: given an initial scene of reconstructed objects, it infers how objects support one another and settles them one by one in topological order. For each object, SDF-based optimization first resolves penetrations against the already-settled support context, and rigid-body simulation then settles the object into a stable equilibrium under real-world physical constraints. The resulting scene stays aligned to the reference image, with every object resting at a physically valid, stable contact configuration. On the 3D-Front benchmark, $ϕ$-Scene achieves the strongest overall performance among out-of-domain methods and remains highly competitive with in-domain baselines on standard reconstruction metrics. Human and MLLM studies prefer $ϕ$-Scene in visual quality, reference alignment, and physical plausibility. Dedicated physical metrics show that it substantially reduces penetration artifacts and yields much lower post-simulation drift. To our knowledge, $ϕ$-Scene is among the first image-to-3D scene reconstruction methods that explicitly reaches dynamic rigid-body equilibrium while preserving reference alignment.
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