We tackle the task of multi-view, multi-person 3D human pose estimation from
a limited number of uncalibrated depth cameras. Recently, many approaches have
been proposed for 3D human pose estimation from multi-view RGB cameras.
However, these works (1) assume the number of RGB camera views is large enough
for 3D reconstruction, (2) the cameras are calibrated, and (3) rely on ground
truth 3D poses for training their regression model. In this work, we propose to
leverage sparse, uncalibrated depth cameras providing RGBD video streams for 3D
human pose estimation. We present a simple pipeline for Multi-View Depth Human
Pose Estimation (MVD-HPE) for jointly predicting the camera poses and 3D human
poses without training a deep 3D human pose regression model. This framework
utilizes 3D Re-ID appearance features from RGBD images to formulate more
accurate correspondences (for deriving camera positions) compared to using
RGB-only features. We further propose (1) depth-guided camera-pose estimation
by leveraging 3D rigid transformations as guidance and (2) depth-constrained 3D
human pose estimation by utilizing depth-projected 3D points as an alternative
objective for optimization. In order to evaluate our proposed pipeline, we
collect three video sets of RGBD videos recorded from multiple sparse-view
depth cameras and ground truth 3D poses are manually annotated. Experiments
show that our proposed method outperforms the current 3D human pose
regression-free pipelines in terms of both camera pose estimation and 3D human
pose estimation.

利用稀疏的未校准深度相机提供的 RGBD 视频流，我们提出了一种简单的多视角深度人体姿态估计 (MVD-HPE) 管线，用于联合预测相机姿态和 3D 人体姿态，无需训练深度 3D 人体姿态回归模型。

多视角非标定深度相机下的多人三维姿态估计

Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth  Cameras

The ultimate goal of this indoor mapping research is to automatically
reconstruct a floorplan simply by walking through a house with a smartphone in
a pocket. This paper tackles this problem by proposing FloorNet, a novel deep
neural architecture. The challenge lies in the processing of RGBD streams
spanning a large 3D space. FloorNet effectively processes the data through
three neural network branches: 1) PointNet with 3D points, exploiting the 3D
information; 2) CNN with a 2D point density image in a top-down view, enhancing
the local spatial reasoning; and 3) CNN with RGB images, utilizing the full
image information. FloorNet exchanges intermediate features across the branches
to exploit the best of all the architectures. We have created a benchmark for
floorplan reconstruction by acquiring RGBD video streams for 155 residential
houses or apartments with Google Tango phones and annotating complete floorplan
information. Our qualitative and quantitative evaluations demonstrate that the
fusion of three branches effectively improves the reconstruction quality. We
hope that the paper together with the benchmark will be an important step
towards solving a challenging vector-graphics reconstruction problem. Code and
data are available at this https URL

本研究旨在通过智能手机，利用深度神经网络技术重建室内地图，提出了线上勘测算法 FloorNet 核心思路，即将 RGBD 流图像数据分别经过三个大的神经网络模块来提取特征并同时考虑 3D 与 2D 空间信息，这种方法能够有效提高室内平面图的重建准确性。