This paper presents a neural network based semantic plane detection method
utilizing polygon representations. The method can for example be used to solve
room layout estimations tasks. The method is built on, combines and further
develops several different modules from previous research. The network takes an
RGB image and estimates a wireframe as well as a feature space using an
hourglass backbone. From these, line and junction features are sampled. The
lines and junctions are then represented as an undirected graph, from which
polygon representations of the sought planes are obtained. Two different
methods for this last step are investigated, where the most promising method is
built on a heterogeneous graph transformer. The final output is in all cases a
projection of the semantic planes in 2D. The methods are evaluated on the
Structured 3D dataset and we investigate the performance both using sampled and
estimated wireframes. The experiments show the potential of the graph-based
method by outperforming state of the art methods in Room Layout estimation in
the 2D metrics using synthetic wireframe detections.

该论文提出了一种基于神经网络和多边形表示的语义平面检测方法，可用于解决房间布局估计任务，通过线和交点等特征生成无向图，从中获得所求平面的多边形表示，实验结果表明该基于图的方法在使用合成线框检测时超越了当今最先进的方法，表现出很大的潜力。

利用异构图和线框进行室内布局估计的多边形检测

Polygon Detection for Room Layout Estimation using Heterogeneous Graphs  and Wireframes

We present a novel framework named PlaneMVS for 3D plane reconstruction from
multiple input views with known camera poses. Most previous learning-based
plane reconstruction methods reconstruct 3D planes from single images, which
highly rely on single-view regression and suffer from depth scale ambiguity. In
contrast, we reconstruct 3D planes with a multi-view-stereo (MVS) pipeline that
takes advantage of multi-view geometry. We decouple plane reconstruction into a
semantic plane detection branch and a plane MVS branch. The semantic plane
detection branch is based on a single-view plane detection framework but with
differences. The plane MVS branch adopts a set of slanted plane hypotheses to
replace conventional depth hypotheses to perform plane sweeping strategy and
finally learns pixel-level plane parameters and its planar depth map. We
present how the two branches are learned in a balanced way, and propose a
soft-pooling loss to associate the outputs of the two branches and make them
benefit from each other. Extensive experiments on various indoor datasets show
that PlaneMVS significantly outperforms state-of-the-art (SOTA) single-view
plane reconstruction methods on both plane detection and 3D geometry metrics.
Our method even outperforms a set of SOTA learning-based MVS methods thanks to
the learned plane priors. To the best of our knowledge, this is the first work
on 3D plane reconstruction within an end-to-end MVS framework.

本文提出了一种名为 PlaneMVS 的新型框架，用于从具有已知相机姿态的多个输入视图中重建 3D 平面。采用多视角几何的 MVS 流水线将平面重建解耦，采用倾斜平面假设来取代传统深度假设并最终学习像素级平面参数及其平面深度图。通过大量室内数据集的实验表明，PlaneMVS 在平面检测和 3D 几何度量上显著优于最先进的单视角平面重建方法。