  Point cloud based methods have produced promising results in areas such as 3D
object detection in autonomous driving. However, most of the recent point cloud
work focuses on single depth sensor data, whereas less work has been done on
indoor monitoring applications, such as operation room monitoring in hospitals
or indoor surveillance. In these scenarios multiple cameras are often used to
tackle occlusion problems. We propose an end-to-end multi-person 3D pose
estimation network, Point R-CNN, using multiple point cloud sources. We conduct
extensive experiments to simulate challenging real world cases, such as
individual camera failures, various target appearances, and complex cluttered
scenes with the CMU panoptic dataset and the MVOR operation room dataset.
Unlike most of the previous methods that attempt to use multiple sensor
information by building complex fusion models, which often lead to poor
generalization, we take advantage of the efficiency of concatenating point
clouds to fuse the information at the input level. In the meantime, we show our
end-to-end network greatly outperforms cascaded state-of-the-art models.
