Reconstruction and Registration of Large-Scale Medical Scene Using Point Clouds Data from Different Modalities

TL;DR

This paper presents a method for fusing large-scale 3D medical scene data from different sensors, improving scene reconstruction and registration accuracy for surgical monitoring.

Contribution

It introduces a system combining Lidar and Kinect data for detailed large-scale medical scene reconstruction and a fast registration algorithm for multi-modality data alignment.

Findings

Reconstructed detailed large-scale medical scenes from fused sensor data.

Achieved accurate registration of multi-modality 3D datasets.

Enhanced surgical scene monitoring capabilities.

Abstract

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.