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Published Articles >> Table of Contents >> Abstract
Second International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04)
pp. 308-315
Neural Mesh Ensembles
Ioannis Ivrissimtzis, MPI Informatik
Yunjin Lee, POSTECH & MPI Informatik
Seungyong Lee, POSTECH & MPI Informatik
Won-Ki Jeong, University of Utah
Hans-Peter Seidel, MPI Informatik
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TDPVT.2004.1335216
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| Abstract |
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This paper proposes the use of neural network ensembles
to boost the performance of a neural network based
surface reconstruction algorithm. Ensemble is a very popular
and powerful statistical technique based on the idea
of averaging several outputs of a probabilistic algorithm.
In the context of surface reconstruction, two main problems
arise. The first is finding an efficient way to average meshes
with different connectivity, and the second is tuning the parameters
for surface reconstruction to maximize the performance
of the ensemble. We solve the first problem by voxelizing
all the meshes on the same regular grid and taking
majority vote on each voxel. We tune the parameters experimentally,
borrowing ideas from weak learning methods.
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 Additional Information
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Citation:
Ioannis Ivrissimtzis, Yunjin Lee, Seungyong Lee, Won-Ki Jeong, Hans-Peter Seidel,
"Neural Mesh Ensembles,"
3dpvt,
pp. 308-315,
Second International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04),
2004
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