Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology

Bruno Vallet,Stephane Guinard

doi:10.52638/rfpt.2018.412

Abstract

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D points 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 posible 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.

Highlights

LiDAR scanning technologies have become a widespread and direct mean for acquiring a precise sampling of the geometry of scenes of interest
Surface reconstruction generally aims at reconstructing triangulated surface meshes from point clouds, as they are the most common numerical representation for surfaces in 3D, well adapted for further processing
Sensor (2) Echo sensor topology : a 6- each echo is connected to all echoes of the 6 neighboring pulses

Summary

Introduction

LiDAR scanning technologies have become a widespread and direct mean for acquiring a precise sampling of the geometry of scenes of interest. Unlike images, LiDAR point clouds do not have a natural topology (4- or 8-neighborhoods for images) allowing to recover the continuous nature of the acquired scenes from the individual samples. This is why a large amount of research work has been dedicated into recovering a continuous surface from a cloud of point samples, which is a central problem in geometry processing (Hoppe et al, 1992; Podolak and Rusinkiewicz, 2005; Hornung and Kobbelt, 2006). This allows a continuous representation of the real surface, and enables the appraisal of occlusions, to render only the visible parts of the scene (Arikan et al, 2014)

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Conclusion