Consistent Propagation of Normal Orientations in Point Clouds

Many algorithms for point cloud processing especially surface reconstruction rely on normal information available at each point. Normal directions are typically taken from a local tangent plane approximation which is obtained by fitting a surface model to the neighboring point samples. While the direction can be estimated locally, finding a consistent normal orientation over the whole surface is only possible in a global context. Existing methods for this problem can be classified into volumetric and propagation based approaches. Volumetric methods are trying to divide the space into inside and outside regions which is often complicated to implement and have problems with open surfaces and large holes. Propagation based methods can deal with open surfaces but often fail on sharp features. This paper analyses the behavior of surficial orientation methods, gives a better understanding of the underlying model assumptions of existing techniques and proposes a novel and improved propagation heuristic.