We focus on estimating the 3D orientation of the ground plane from a single
image. We formulate the problem as an inter-mingled multi-task prediction
problem by jointly optimizing for pixel-wise surface normal direction, ground
plane segmentation, and depth estimates. Specifically, our proposed model,
GroundNet, first estimates the depth and surface normal in two separate
streams, from which two ground plane normals are then computed
deterministically. To leverage the geometric correlation between depth and
normal, we propose to add a consistency loss on top of the computed ground
plane normals. In addition, a ground segmentation stream is used to isolate the
ground regions so that we can selectively back-propagate parameter updates
through only the ground regions in the image. Our method achieves the
top-ranked performance on ground plane normal estimation and horizon line
detection on the real-world outdoor datasets of ApolloScape and KITTI,
improving the performance of previous art by up to 17.7% relatively.

GroundNet 模型通过多任务联合优化像素级表面法线方向、地面平面分割和深度估计，实现从单张图像中估算出地面平面的 3D 方向，通过加入一致性损失优化了地面平面法线预测，并在 ApolloScape 和 KITTI 等真实世界数据集上取得了顶尖水平。

GroundNet: 基于单目相机的地面平面法向估计与几何一致性

GroundNet: Monocular Ground Plane Normal Estimation with Geometric  Consistency

We introduce GroundNet, a neural network for referring expression recognition
-- the task of localizing (or grounding) in an image the object referred to by
a natural language expression. Our approach to this task is the first to rely
on a syntactic analysis of the input referring expression in order to inform
the structure of the computation graph. Given a parse tree for an input
expression, we explicitly map the syntactic constituents and relationships
present in the tree to a composed graph of neural modules that defines our
architecture for performing localization. This syntax-based approach aids
localization of \textit{both} the target object and auxiliary supporting
objects mentioned in the expression. As a result, GroundNet is more
interpretable than previous methods: we can (1) determine which phrase of the
referring expression points to which object in the image and (2) track how the
localization of the target object is determined by the network. We study this
property empirically by introducing a new set of annotations on the GoogleRef
dataset to evaluate localization of supporting objects. Our experiments show
that GroundNet achieves state-of-the-art accuracy in identifying supporting
objects, while maintaining comparable performance in the localization of target
objects.

GroundNet 是用于语言表达识别的神经网络，利用句法分析输入指代表达式以指导计算图的结构，并解析句法成分和关系，映射到神经元模块组成的图形结构以进行目标物体及相关支持物体的定位，具有更好的可解释性和鲁棒性。