Full 3D estimation of human pose from a single image remains a challenging
task despite many recent advances. In this paper, we explore the hypothesis
that strong prior information about scene geometry can be used to improve pose
estimation accuracy. To tackle this question empirically, we have assembled a
novel $\textbf{Geometric Pose Affordance}$ dataset, consisting of multi-view
imagery of people interacting with a variety of rich 3D environments. We
utilized a commercial motion capture system to collect gold-standard estimates
of pose and construct accurate geometric 3D CAD models of the scene itself.
To inject prior knowledge of scene constraints into existing frameworks for
pose estimation from images, we introduce a novel, view-based representation of
scene geometry, a $\textbf{multi-layer depth map}$, which employs multi-hit ray
tracing to concisely encode multiple surface entry and exit points along each
camera view ray direction. We propose two different mechanisms for integrating
multi-layer depth information pose estimation: input as encoded ray features
used in lifting 2D pose to full 3D, and secondly as a differentiable loss that
encourages learned models to favor geometrically consistent pose estimates. We
show experimentally that these techniques can improve the accuracy of 3D pose
estimates, particularly in the presence of occlusion and complex scene
geometry.

通过使用基于视觉的场景几何形状信息，我们提出了一种用于改善人体姿态估计的新颖机制：多层深度图。我们展示了这种方法可以提高 3D 姿态估计的精度。

几何姿态可供性：带场景约束的三维人体姿态

Geometric Pose Affordance: 3D Human Pose with Scene Constraints

This paper considers object detection and 3D estimation using an FMCW radar.
The state-of-the-art deep learning framework is employed instead of using
traditional signal processing. In preparing the radar training data, the ground
truth of an object orientation in 3D space is provided by conducting image
analysis, of which the images are obtained through a coupled camera to the
radar device. To ensure successful training of a fully convolutional network
(FCN), we propose a normalization method, which is found to be essential to be
applied to the radar signal before feeding into the neural network. The system
after proper training is able to first detect the presence of an object in an
environment. If it does, the system then further produces an estimation of its
3D position. Experimental results show that the proposed system can be
successfully trained and employed for detecting a car and further estimating
its 3D position in a noisy environment.

本文使用深度学习框架对 FMCW 雷达进行物体检测和 3D 估计，在处理雷达训练数据时，通过图像分析提供三维空间中物体方向的真实值，并提出归一化方法以确保成功训练完全卷积网络 (FCN)，该系统能够在嘈杂环境中成功检测到汽车并进一步估计其 3D 位置。