A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD
- 1Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen, FJ 361005, China
- 2Mobile Mapping Lab, Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- 3School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA
- 4Xizang Key Laboratory of Optical Information Processing and Visualization Technology, Information Engineering College, Xizang Minzu University, Xianyang, SX 712082, China
Keywords: Partial Similarity, 3D Point Cloud, 3D Mesh, Laser Scanning, 3D Object Retrieval, Weighted Hausdorff Distance
Abstract. This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.