AirPlanes：通过三维一致性嵌入实现精确平面估计
AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
AI总结：
本文提出AirPlanes方法，通过预测多视图一致的平面嵌入来补充几何信息，在ScanNetV2上显著提升了从已定位图像中估计平面表面的准确性。
摘要：
从三维场景中提取平面对机器人和增强现实中的下游任务非常有用。本文研究从已定位图像中估计场景平面表面的问题。我们的第一个发现是，将流行的聚类算法与三维几何估计的最新进展相结合，可以得到一个出人意料地具有竞争力的基线。然而，这种纯几何方法显然对平面语义不敏感，而平面语义对于区分不同平面至关重要。为克服这一局限，我们提出了一种方法，预测多视图一致的平面嵌入，在将点聚类为平面时对几何信息进行补充。我们在ScanNetV2数据集上的大量评估表明，对于平面估计任务，我们的新方法优于现有方法以及我们强大的几何基线。
英文摘要：
Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However, such purely geometric methods are understandably oblivious to plane semantics, which are crucial to discerning distinct planes. To overcome this limitation, we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.
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。
