  We present UniPlane, a novel method that unifies plane detection and
reconstruction from posed monocular videos. Unlike existing methods that detect
planes from local observations and associate them across the video for the
final reconstruction, UniPlane unifies both the detection and the
reconstruction tasks in a single network, which allows us to directly optimize
final reconstruction quality and fully leverage temporal information.
Specifically, we build a Transformers-based deep neural network that jointly
constructs a 3D feature volume for the environment and estimates a set of
per-plane embeddings as queries. UniPlane directly reconstructs the 3D planes
by taking dot products between voxel embeddings and the plane embeddings
followed by binary thresholding. Extensive experiments on real-world datasets
demonstrate that UniPlane outperforms state-of-the-art methods in both plane
detection and reconstruction tasks, achieving +4.6 in F-score in geometry as
well as consistent improvements in other geometry and segmentation metrics.
