Modelling cloud data using an adaptive slicing approach
In reverse engineering, the conventional surface modelling from point cloud data is time-consuming and requires expert modelling skills. One of the innovative modelling methods is to directly slice the point cloud along a direction and generate a layer-based model, which can be used directly for fabrication using rapid prototyping (RP) techniques. However, the main challenge is that the thickness of each layer must be carefully controlled so that each layer will yield the same shape error, which is within the given tolerance bound. In this paper, an adaptive slicing method for modelling point cloud data is presented. It seeks to generate a direct RP model with minimum number of layers based on a given shape error. The method employs an iterative approach to find the maximum allowable thickness for each layer. Issues including multiple loop segmentation in layers, profile curve generation, and data filtering, are discussed. The efficacy of the algorithm is demonstrated by case studies.
cloud data reverse engineering layer-based model rapid prototyping shape-error