Evaluation of 3D point cloud distances: A comparative study in multi-point cloud fusion environments

In the domain of 3D shape reconstruction and metrology, the precise alignment and measurement of point clouds is critical, especially within the context of industrial inspection where accuracy requirements are high. This work addresses challenges stemming from intricate object properties, including complex geometries or surfaces, resulting in diverse artefacts, holes, or sparse point clouds. We present a comprehensive evaluation of point cloud measurement metrics on different object shapes and error patterns. We focus on the task of point cloud evaluation of objects to assess their quality. This is achieved through the acquisition of partial point clouds acquired from multiple perspectives. This is followed by a point cloud fusion process including an initial alignment and a point cloud refinement step. We evaluate these point clouds with respect to a reference sampled point cloud and mesh. In this work, we evaluate various point cloud metrics across experimentally relevant scenarios like cloud density variations, different noise levels, and hole sizes on objects with different geometries. We additionally show how the approach can be applied in industrial object evaluation.