Reconstructing noisy LIDAR scans with spline smoothing
This example illustrates SilverLining's spline-smoothing technique for automatically fitting a surface to noisy LIDAR scan data of a
well-known fountain in Santa Barbara.
Spline smoothing is a technique for fitting a surface which approximates at the raw data rather than exactly interpolating. A single parameter allows the user to balance fidelity to the original data with smoothness of the fitted surface.
- The statue is approximately 2m x
5m and was scanned using a Cyrax 2400 scanner.
- The data set consists of 350,000
points imaged from several viewpoints in front of the
statue
- Three spheres apparent in the
image were used to align the scans
- Data courtesy of Allen
Instruments & Supplies, 1474 Theresa St, Carpinteria,
CA93013, USA
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350,000 point cloud taken from
several scanner positions in front of the statue.
Note the large occluded regions
where no data has been recorded.
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The automatically fitted RBF
surface.
The data fidelity constraint has
been chosen to reflect the estimated level of noise in the data,
and hence reconstruct a smooth surface despite the noisy
data.
The function fitted to the has preserved the topology of features such as the arms
and the gap between the arms and the rest of the statue, despite
having no data in these regions. Even the reference spheres have
been correctly reconstructed despite only a partial hemisphere
being imaged by the scanner.
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Despite having data only from the front view of the statue, the
smoothness constraint inherent in the thin-plate basis has
correctly preserved the gap between the arm and the rest of the
statue. |
Detail of corresponding smooth fit. The gap between the arm and
the statue is still preserved. |
Spline smoothing
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Exact fit. |
Medium amount of smoothing applied.
The RBF approximates at data points. |
Increased smoothing. |
