Abstract
With the current transition of various digital contents from 2D to 3D, the problem of 3D data matching and registration is increasingly important. Registration of multimodal 3D data acquired from different sensors remains a challenging problem due to the difference in types and characteristics of the data. In this paper, we evaluate the registration performance of 3D feature descriptors with different domains on datasets from various environments and modalities. Datasets are acquired in indoor and outdoor environments with 2D and 3D sensing devices including LIDAR, spherical imaging, digital camera and RGBD camera. FPFH, PFH and SHOT feature descriptors are applied to the 3D point clouds generated from the multi-modal datasets. Local neighbouring point distribution, keypoints distribution, colour information and their combinations are used for feature description. Finally we analyse their influences on the multi-modal 3D point clouds data registration.