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.

本文提出一种新方法，通过预测多视角一致的平面嵌入来辅助几何方法，将点进行聚类成平面，从而解决了在场景中估计平面表面的问题，并在 ScanNetV2 数据集上表明，该方法优于现有方法和强基线的平面估计任务。