We propose to combine recent Convolutional Neural Networks (CNN) models with
depth imaging to obtain a reliable and fast multi-person pose estimation
algorithm applicable to Human Robot Interaction (HRI) scenarios. Our hypothesis
is that depth images contain less structures and are easier to process than RGB
images while keeping the required information for human detection and pose
inference, thus allowing the use of simpler networks for the task. Our
contributions are threefold. (i) we propose a fast and efficient network based
on residual blocks (called RPM) for body landmark localization from depth
images; (ii) we created a public dataset DIH comprising more than 170k
synthetic images of human bodies with various shapes and viewpoints as well as
real (annotated) data for evaluation; (iii) we show that our model trained on
synthetic data from scratch can perform well on real data, obtaining similar
results to larger models initialized with pre-trained networks. It thus
provides a good trade-off between performance and computation. Experiments on
real data demonstrate the validity of our approach.

本文提出一种利用深度图像结合卷积神经网络实现快速且可靠的多人姿态估计算法的方案，可以有效地应用于人机交互领域；具体贡献包括提出了基于残差块的快速网络、构建了包括 170k 多张人体合成图像与真实标注数据在内的公开数据集用于评估，证明了该模型在真实数据上性能良好。

基于深度的实时卷积网络人体姿势估计

Real-time Convolutional Networks for Depth-based Human Pose Estimation

One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. In this paper, we impose multiple projection patterns into each single
captured image to realize temporal super resolution of the depth image
sequences. With our method, multiple patterns are projected onto the object
with higher fps than possible with a camera. In this case, the observed pattern
varies depending on the depth and motion of the object, so we can extract
temporal information of the scene from each single image. The decoding process
is realized using a learning-based approach where no geometric calibration is
needed. Experiments confirm the effectiveness of our method where sequential
shapes are reconstructed from a single image. Both quantitative evaluations and
comparisons with recent techniques were also conducted.

本文提出一种多个投影图案的图像重建方法，可以在不需要几何标定的情况下实现一张图像内多组深度图像序列，实验结果表明该方法具有出色的效果和良好的实用性。

利用高帧率结构光进行帧内运动编码的时间形状超分辨率

Temporal shape super-resolution by intra-frame motion encoding using  high-fps structured light

We propose a computational model for shape, illumination and albedo inference
in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on
phase modulation, our camera enables general exposure profiles. This results in
added flexibility and requires novel computational approaches.
To address this challenge we propose a generative probabilistic model that
accurately relates latent imaging conditions to observed camera responses.
While principled, realtime inference in the model turns out to be infeasible,
and we propose to employ efficient non-parametric regression trees to
approximate the model outputs. As a result we are able to provide, for each
pixel, at video frame rate, estimates and uncertainty for depth, effective
albedo, and ambient light intensity. These results we present are
state-of-the-art in depth imaging.
The flexibility of our approach allows us to easily enrich our generative
model. We demonstrate that by extending the original single-path model to a
two-path model, capable of describing some multipath effects. The new model is
seamlessly integrated in the system at no additional computational cost.
Our work also addresses the important question of optimal exposure design in
pulsed TOF systems. Finally, for benchmark purposes and to obtain realistic
empirical priors of multipath and insights into this phenomena, we propose a
physically accurate simulation of multipath phenomena.

本篇论文提出了一种计算模型，将激光飞行时间相机的形状、照明和反照率推断联系起来，并通过有效的非参数回归树近似模型输出，为每个像素提供深度、等效反照率和环境光强度的估计和不确定性。同时，通过拓展单路径模型为双路径模型，无需额外计算成本即可描述多径效应。