Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology

TL;DR

This paper introduces a novel method for reconstructing weighted simplicial complexes from mobile laser scanning data by leveraging sensor topology and spatial relationships, improving scene representation accuracy.

Contribution

The method uniquely incorporates sensor topology and distance-based weighting to enhance simplicial complex reconstruction from MLS point clouds.

Findings

Improved accuracy over unweighted reconstructions

Effective filtering of connections based on collinearity and perpendicularity

Enhanced scene modeling with weighted simplicial complexes

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 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, weighted according to its distance to the sensor, and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create and filter triangles for each triplet of self-connected edges and according to their local planarity. We compare our results to an unweighted simplicial complex reconstruction.