SIGnificant Outlier Removal

Abstract

Point clouds, usually obtained through scanning or various image processing, are commonly affected by noise and outliers. Such artifacts compromise data quality as they significantly distort subsequent processes, such as normal estimation and surface reconstruction. In this work, we introduce a proximity-based outlier removal method for point clouds. We improve on statistical methods based on neighboring graphs by using a parameter-free proximity graph—the spheres-of-influence (SIG), thus requiring fewer parameters compared to classical methods and obtaining better results. Moreover, the simplicity of our method allows it to become an easy replacement for existing statistical methods.