A quantitative evaluation of surface normal estimation in point clouds

We revisit a well-studied problem in the analysis of range data: surface normal estimation for a set of unorganized points. Surface normal estimation has been well-studied initially due to its theoretical appeal and more recently due to its many practical applications. The latter cover several aspects of range data analysis from plane or surface fitting to segmentation, object detection and scene analysis. Following the vast majority of the literature, we also focus our attention on techniques that operate in small neighborhoods around the point whose normal is to be estimated. We pay close attention to aspects of the implementation, such as the use of weights and normalization, that have not been studied in detail in the past. We perform quantitative evaluation on a diverse set of point clouds derived from 3D meshes, which allows us to obtain accurate ground truth.