Despite rapid progress in scene segmentation in recent years, 3D segmentation methods are still limited when there is severe occlusion. The key challenge is estimating the segment boundaries of (partially) occluded objects, which are inherently ambiguous when considering only a single frame. In this work, we propose Multihypothesis Segmentation Tracking (MST), a novel method for volumetric segmentation in changing scenes, which allows scene ambiguity to be tracked and our estimates to be adjusted over time as we interact with the scene. Two main innovations allow us to tackle this difficult problem: 1) A novel way to sample possible segmentations from a segmentation tree; and 2) A novel approach to fusing tracking results with multiple segmentation estimates. These methods allow MST to track the segmentation state over time and incorporate new information, such as new objects being revealed. We evaluate our method on several cluttered tabletop environments in simulation and reality. Our results show that MST outperforms baselines in all tested scenes.

翻译：尽管近些年来在场景分解方面进展迅速,但当存在严重的分解现象时,三维分解方法仍然有限。关键的挑战是如何估计(部分)隐蔽物体的部位界限,这些物体在考虑单一框架时本身就具有模糊性。在这项工作中,我们提议多假肢分解跟踪(MST),这是在变化的场景中进行体积分解的一种新颖方法,可以追踪现场的模糊性,并在我们与场景互动时随时调整我们的估计数。两个主要创新使我们得以解决这一困难问题:(1) 从分解树中抽样可能的部位界限的新办法;和(2) 利用多分解估计的跟踪结果的新办法。这些方法使得多环形分解跟踪可以跟踪分解状态,并纳入新的信息,例如新显示的物体。我们在模拟和现实中评估了我们在若干片状桌面环境中的方法。我们的结果显示,MST在所有测试的场景中都超越了基线。