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.

本文提出了一种新的基于深度学习的方法，用于估计给定一对 360 全景图的房间布局，并使用称为 PSMNet 的系统，其中包含一个端到端的联合布局 - 姿态估计器，能够显著优于现有的布局估计器，特别是在大型和复杂的房间空间中。

PSMNet: 基于位置感知的立体融合网络用于室内布局估计

PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation

Safe autonomous driving requires reliable 3D object detection-determining the
6 DoF pose and dimensions of objects of interest. Using stereo cameras to solve
this task is a cost-effective alternative to the widely used LiDAR sensor. The
current state-of-the-art for stereo 3D object detection takes the existing
PSMNet stereo matching network, with no modifications, and converts the
estimated disparities into a 3D point cloud, and feeds this point cloud into a
LiDAR-based 3D object detector. The issue with existing stereo matching
networks is that they are designed for disparity estimation, not 3D object
detection; the shape and accuracy of object point clouds are not the focus.
Stereo matching networks commonly suffer from inaccurate depth estimates at
object boundaries, which we define as streaking, because background and
foreground points are jointly estimated. Existing networks also penalize
disparity instead of the estimated position of object point clouds in their
loss functions. We propose a novel 2D box association and object-centric stereo
matching method that only estimates the disparities of the objects of interest
to address these two issues. Our method achieves state-of-the-art results on
the KITTI 3D and BEV benchmarks.

研究采用一种新型 2D 框和以物体为中心的立体匹配方法，通过测量目标物体的视差来解决当前立体匹配网络固有的偏差问题，从而实现自主驾驶中的 3D 对象检测。

Object-Centric Stereo Matching for 3D Object Detection

Recent work has shown that depth estimation from a stereo pair of images can
be formulated as a supervised learning task to be resolved with convolutional
neural networks (CNNs). However, current architectures rely on patch-based
Siamese networks, lacking the means to exploit context information for finding
correspondence in illposed regions. To tackle this problem, we propose PSMNet,
a pyramid stereo matching network consisting of two main modules: spatial
pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage
of the capacity of global context information by aggregating context in
different scales and locations to form a cost volume. The 3D CNN learns to
regularize cost volume using stacked multiple hourglass networks in conjunction
with intermediate supervision. The proposed approach was evaluated on several
benchmark datasets. Our method ranked first in the KITTI 2012 and 2015
leaderboards before March 18, 2018. The codes of PSMNet are available at:
this https URL

该论文提出了一种新的算法 PSMNet，通过空间金字塔池化模块和三维卷积神经网络来提取全局上下文信息并形成代价体积，通过多层次网络及中间监督来优化代价体积，该算法在 KITTI 2012 和 2015 数据集中的表现也十分优秀。