surface reconstruction from point clouds - mit csail · 2007. 9. 18. · initial detection...
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![Page 1: Surface Reconstruction from Point Clouds - MIT CSAIL · 2007. 9. 18. · Initial Detection (“Pop-out”) Ballooning Surface Reconstruction Surface Segmentation Recognition Multiple](https://reader035.vdocuments.mx/reader035/viewer/2022063008/5fbb7ca92afb876008158237/html5/thumbnails/1.jpg)
Gerald DalleySignal Analysis and Machine Perception Laboratory
The Ohio State University
07 Feb 2002
Linux Clustering Software
+
Surface Reconstruction
from Point Clouds
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Topic Motivation
• Computationally
intensive work
• Many “small”
execution jobs at
each phase
Range Image
Aquisition
Initial Detection
(“Pop-out”)
Ballooning
Surface
Reconstruction
Surface
Segmentation
Recognition
Multiple balloons
must be merged:
• Segmentation requires
dense meshes
• Non-sparse balloons
are “leaky”
• Merging overlapping
balloons is non-trivial
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Outline
• Part 1: Linux Clustering Software
– ClusterIt
– Portable Batch System
• Part 2: Surface Reconstruction
– Preliminaries
– Curve Reconstruction
– Cocone Algorithm
– Undersampling
– Some Results
• Current Status
• Further Reading
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Cluster Software:
ClusterIt
• What is it?
– Simultaneous execution on a set of Linux boxes
– Execution on “any” Linux box (but PBS is much better)
• What do I have to do to use it?
– See http://sampl.eng.ohio-state.edu/~dalleyg/faq/index.html for
instructions on configuring SSH
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Cluster Software:
SAMPL Cluster Groups
See /etc/dsh.cluster on a Linux box for the full set of groups
Group Comments
a All processors (use with jsd, etc.)
a1 All boxes (use with dsh, etc.)
b All Beowulf processors
b1 All Beowulf boxes
m All of the Micron boxes
dc All processors on the dual Celerons
dc1 All dual Celeron boxes
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Cluster Software:
dsh• When would I use it?
– Same command, many machines
– Batch job preparation
– Administrative work
• Usage
– dsh -g om 'hostname -i'
– Results
sampl01: 164.107.162.9
sampl02: 164.107.162.10
sampl10: 164.107.162.18
sampl12: 164.107.162.20
Group (required!) Remote command (quoted)
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Cluster Software:
dsh Examples
• Good examples– dsh -g om 'hostname -i'
– dsh –g om 'hostname > /tmp/tmpfile'
– dsh –g a1 'smbmount //samplf04/dalleyg
/u/dalleyg -o umask=077,dmask=077,
password=PASSWORD'
• What to be careful about
– Using quotes (single or double)
– Redirecting input or output
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Cluster Software:
Other ClusterIt Examples
• pcp -g a1 datafile /tmp/datafile
– Copies file datafile from the current directory to
/tmp/datafile on all machines in the a1 group
– Useful for copying files to local temporary storage
• prm -g a1 /tmp/datafile
– Deletes /tmp/datafile on all machines in the a1
group
– Useful for cleanup of local temporary storage, etc.
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Cluster Software:
Portable Batch System
• What is it?
– Manages submission and execution of large
batch jobs
– Allows balancing between users
– Used for single multi-processor machines all
the way up to compute farms with many Crays
(e.g. OSC)
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Cluster Software:
Portable Batch System (cont’d.)
• Submitting a job (from eepc359 only)*
– qsub -N jobname jobprogram
– qsub -N Test /home/dalleyg/nfs/test2.pl
• Cluster status
– qstat -f -B
• xpbs
– Helps you create job scripts, configure advanced options, etc.
• xpbsmon
– Helps you monitor the state of the machines on the cluster
*The configuration is still being debugged.
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Part 2:
Surface Reconstruction…
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Surface Reconstruction Preliminaries:
Voronoi Diagrams
• Voronoi Cell of x
– The set of points
that are closer to x
than to any other
sample point
Voronoi cell
Voronoi edge
Voronoi vertex
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Surface Reconstruction Preliminaries:
Preliminaries: Medial Axis
• Medial Axis:
– Find all circles that tangentially touch the curve in at least 2 points
– Medial axis = centers of all those circles
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Surface Reconstruction Preliminaries:
ε-sampling
f(x) ≡ feature size at point x
= distance to the medial axis at point x
Sampling criterion: each sample point x is at most εf(x) from the next closest sample (0 < ε < 1, typically).
Important note: When ε is small, the curve locally looks flat
f(x)
x
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Surface Reconstruction:
Curve Reconstruction
• Algorithm:
– Find the closest point, p, to x and connect them
– Find the closest point, q, to x such that the angle pxq is
at least 90°.
• Guaranteed to work when ε ≤ ⅓
x
p
q
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Surface Reconstruction:
Cocone Algorithm
• p+ ≡ pole of p = point in the
Voronoi cell farthest from p
• ε < 0.1 →
– the vector from p to p+ is
within π/8 of the true surface
normal
– The surface is nearly flat
within the cell Voronoi cell of p
p+
p
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Sample Reconstructed Surfaces
Foot images from Prof. Dey
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TuT Status
Range Image
Aquisition
Initial Detection
(“Pop-out”)
Ballooning
Surface
Reconstruction
Surface
Segmentation
Recognition
Software written
See Kanu
Software written (by Kanu), generating data
Software written (by Prof. Dey)
Evaluating several variants
To be done
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Further Reading
– Clusterit• http://www.garbled.net/clusterit.html
• http://sampl.eng.ohio-state.edu/~dalleyg/faq/index.html
– Portable Batch System• http://www.openpbs.org
– Official web site (ask if you need the password)
• http://www-itg.lbl.gov/Grid/public/pbs/pbs.v2.3_admin.pdf
– Detailed administration/usage guide
• http://www-math.cudenver.edu/~jmandel/mri/Schedulers-overview.pdf
– Slideshow introduction to PBS by Doug Johnson from the Ohio Supercomputer Center
– Surface Reconstruction• http://www.cis.ohio-state.edu/~tamaldey/
• N. Amenta, S. Choi, T. K. Dey and N. Leekha. A simple algorithm for homeomorphic surface reconstruction. Proc. 16th ACM Symposium on Computational Geometry, 2000, 213--222.