Details
| Original language | English |
|---|---|
| Article number | 5518410 |
| Pages (from-to) | 3204-3221 |
| Number of pages | 18 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 19 |
| Issue number | 12 |
| Publication status | Published - 14 Jun 2010 |
Abstract
Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
Keywords
- Lidar, marked point process, Monte Carlo sampling, object-based stochastic model, source modeling
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IEEE Transactions on Image Processing, Vol. 19, No. 12, 5518410, 14.06.2010, p. 3204-3221.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Marked Point Process for Modeling Lidar Waveforms
AU - Mallet, Clment
AU - Lafarge, Florent
AU - Roux, Michel
AU - Soergel, Uwe
AU - Bretar, Frédéric
AU - Heipke, Christian
PY - 2010/6/14
Y1 - 2010/6/14
N2 - Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
AB - Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
KW - Lidar
KW - marked point process
KW - Monte Carlo sampling
KW - object-based stochastic model
KW - source modeling
UR - http://www.scopus.com/inward/record.url?scp=78649257181&partnerID=8YFLogxK
U2 - 10.1109/TIP.2010.2052825
DO - 10.1109/TIP.2010.2052825
M3 - Article
C2 - 20550992
AN - SCOPUS:78649257181
VL - 19
SP - 3204
EP - 3221
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 12
M1 - 5518410
ER -