Sensor-topology based simplicial complex reconstruction

TL;DR

This paper introduces a novel method for reconstructing simplicial complexes from 3D point clouds obtained via Mobile Laser Scanning, emphasizing local geometric adaptation and topology-based adjacency filtering.

Contribution

The method leverages sensor topology to define adjacency, filters connections based on geometric structures, and enhances reconstruction with regularization for co-planarity and collinearity.

Findings

Improved reconstruction accuracy over naive methods

Effective filtering of connections based on geometric criteria

Enhanced scene representation with regularization techniques

Abstract

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length.