Carnegie Mellon University
Browse
file.pdf (1.55 MB)

Automatic Data Driven Vegetation Modeling for Lidar Simulation

Download (1.55 MB)
journal contribution
posted on 2012-05-01, 00:00 authored by Jean-Emmanuel Deschaud, David Prasser, M. Freddie Dias, Brett Browning, Peter RanderPeter Rander

Traditional lidar simulations render surface models to generate simulated range data. For objects with welldefined surfaces, this approach works well, and traditional 3D scene reconstruction algorithms can be employed to automatically generate the surface models. This approach breaks down, though, for many trees, tall grasses, and other objects with fine-scale geometry: surface models do not easily represent the geometry, and automated reconstruction from real data is difficult. In this paper, we introduce a new stochastic volumetric model that better captures the complexities of real lidar data of vegetation and is far better suited for automatic modeling of scenes from field collected lidar data. We also introduce several methods for automatic modeling and for simulating lidar data utilizing the new model. To measure the performance of the stochastic simulation we use histogram comparison metrics to quantify the differences between data produced by the real and simulated lidar. We evaluate our approach on a range of real world datasets and show improved fidelity for simulating geo-specific outdoor, vegetation scenes.

History

Publisher Statement

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Date

2012-05-01

Usage metrics

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC