  Sensing the medical scenario can ensure the safety during the surgical
operations. So, in this regard, a monitor platform which can obtain the
accurate location information of the surgery room is desperately needed.
Compared to 2D camera image, 3D data contains more information of distance and
direction. Therefore, 3D sensors are more suitable to be used in surgical scene
monitoring. However, each 3D sensor has its own limitations. For example, Lidar
(Light Detection and Ranging) can detect large-scale environment with high
precision, but the point clouds or depth maps are very sparse. As for commodity
RGBD sensors, such as Kinect, can accurately capture denser data, but limited
to a small range from 0.5 to 4.5m. So, a proper method which can address these
problems for fusing different modalities data is important. In this paper, we
proposed a method which can fuse different modalities 3D data to get a
large-scale and dense point cloud. The key contributions of our work are as
follows. First, we proposed a 3D data collecting system to reconstruct the
medical scenes. By fusing the Lidar and Kinect data, a large-scale medical
scene with more details can be reconstructed. Second, we proposed a
location-based fast point clouds registration algorithm to deal with different
modality datasets.
