论文标题
PSMNET：位置感知的立体声合并网络用于房间布局估计
PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
论文作者
论文摘要
在本文中，我们提出了一种新的基于深度学习的方法，用于估计房间布局，并给定一对360个全景。我们的系统称为位置感知的立体声合并网络或PSMNET，是端到端的联合布局置估估计器。 PSMNET由立体声PANO姿势（SP2）变压器和新型的跨观测投影（CP2）层组成。立体视图SP2变压器用于隐式推断视图之间的对应关系，并可以处理嘈杂的姿势。姿势感知的CP2层设计为从相邻视图到锚点（参考）视图的特征，以执行视图融合并估算可见的布局。我们的实验和分析验证了我们的方法，这极大地超过了最新的布局估计器，尤其是对于大型和复杂的房间空间。
In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP2 layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces.
