LitS: A novel Neighborhood Descriptor for Point Clouds

TL;DR

LitS is a new neighborhood descriptor for point clouds that captures local geometry effectively, adaptable to various data conditions, and useful for structural analysis.

Contribution

The paper introduces LitS, a novel piecewise constant function-based neighborhood descriptor for 2D and 3D point clouds, enhancing local geometry characterization.

Findings

LitS effectively captures local neighborhood structures.

LitS is robust to noise and variable density.

LitS can be adapted with parameters for different contexts.

Abstract

With the advancement of 3D scanning technologies, point clouds have become fundamental for representing 3D spatial data, with applications that span across various scientific and technological fields. Practical analysis of this data depends crucially on available neighborhood descriptors to accurately characterize the local geometries of the point cloud. This paper introduces LitS, a novel neighborhood descriptor for 2D and 3D point clouds. LitS are piecewise constant functions on the unit circle that allow points to keep track of their surroundings. Each element in LitS' domain represents a direction with respect to a local reference system. Once constructed, evaluating LitS at any given direction gives us information about the number of neighbors in a cone-like region centered around that same direction. Thus, LitS conveys a lot of information about the local neighborhood of a point, which can be leveraged to gain global structural understanding by analyzing how LitS changes between close points. In addition, LitS comes in two versions ('regular' and 'cumulative') and has two parameters, allowing them to adapt to various contexts and types of point clouds. Overall, they are a versatile neighborhood descriptor, capable of capturing the nuances of local point arrangements and resilient to common point cloud data issues such as variable density and noise.