穿透杂乱：通过迭代物体移除实现结构化3D场景重建
Seeing Through Clutter: Structured 3D Scene Reconstruction via Iterative Object Removal
AI总结：
通过迭代移除物体实现结构化3D场景重建，无需特定任务训练，提升复杂场景下的鲁棒性。
摘要：
我们提出了SeeingThroughClutter，一种通过单张图像分割和建模单个物体来重建结构化3D表示的方法。先前的方法依赖于中间任务如语义分割和深度估计，这些方法在复杂场景中表现不佳，特别是在存在遮挡和杂乱的情况下。我们通过引入一个迭代物体移除和重建管道来解决这个问题，该管道将复杂场景分解为一系列更简单的子任务。使用VLMs作为协调器，通过检测、分割、物体移除和3D拟合逐个移除前景物体。我们证明，移除物体允许对后续物体进行更清晰的分割，即使在高度遮挡的场景中也是如此。我们的方法不需要特定任务的训练，直接受益于基础模型的持续进步。我们在3D-Front和ADE20K数据集上展示了最先进的鲁棒性。项目页面：https://rioak.github.io/seeingthroughclutter/
英文摘要：
We present SeeingThroughClutter, a method for reconstructing structured 3D representations from single images by segmenting and modeling objects individually. Prior approaches rely on intermediate tasks such as semantic segmentation and depth estimation, which often underperform in complex scenes, particularly in the presence of occlusion and clutter. We address this by introducing an iterative object removal and reconstruction pipeline that decomposes complex scenes into a sequence of simpler subtasks. Using VLMs as orchestrators, foreground objects are removed one at a time via detection, segmentation, object removal, and 3D fitting. We show that removing objects allows for cleaner segmentations of subsequent objects, even in highly occluded scenes. Our method requires no task-specific training and benefits directly from ongoing advances in foundation models. We demonstrate stateof-the-art robustness on 3D-Front and ADE20K datasets. Project Page: https://rioak.github.io/seeingthroughclutter/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
