Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still suffers from data scarcity due to annotation difficulty. As such, we address the semi-supervised setting of this task based upon the idea of model exponential moving averaging. But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward. To this end, we define a quad set matching strategy and several consistency losses based upon metrics tailored for layout quads. Besides, we propose a new online pseudo-label harvesting algorithm that decomposes the distribution of a hybrid distance measure between quads and PC into two components. This technique does not need manual threshold selection and intuitively encourages quads to align with reliable layout points. Surprisingly, this framework also works for the fully-supervised setting, achieving a new SOTA on the ScanNet benchmark. Last but not least, we also push the semi-supervised setting to the realistic omni-supervised setting, demonstrating significantly promoted performance on a newly annotated ARKitScenes testing set. Our codes, data and models are released in this repository.

翻译：室内布局估计是一项长期的机器人视觉任务，对环境感知和运动规划均有益处。然而，由于标注困难，基于点云的布局估计仍面临数据稀缺问题。为此，我们基于模型指数移动平均思想，探讨了该任务的半监督设置。但将这一方案适配到基于点云的布局估计最先进方法并非直接可行。鉴于此，我们定义了一种四元组匹配策略，并基于为布局四元组定制的度量设计了几类一致性损失。此外，我们提出了一种新的在线伪标签提取算法，该算法将四元组与点云之间混合距离度量的分布分解为两个分量。该技术无需手动选择阈值，且能直观地促使四元组与可靠的布局点对齐。令人惊讶的是，该框架在全监督设置下同样有效，在ScanNet基准上达到了新的最先进水平。最后但同等重要的是，我们将半监督设置扩展至更现实的全程监督设置，在新标注的ARKitScenes测试集上展示了显著提升的性能。我们的代码、数据和模型已在该仓库中发布。