  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.
