Distributed Volumetric Scene Geometry Reconstruction With a
Network of Distributed Smart Cameras
Abstract
- Central to many problems in scene understanding based on using a
network of tens, hundreds or even thousands of randomly distributed cameras
with on-board processing and wireless communication capability is
the ``efficient'' reconstruction of the 3D geometry structure in the
scene. What is meant by ``efficient'' reconstruction? In this paper
we investigate this from different aspects in the context of visual
sensor networks and offer a distributed reconstruction algorithm
roughly meeting the following goals: 1. Close to achievable 3D
reconstruction accuracy and robustness; 2. Minimization of the
processing time by adaptive computing-job distribution among all the
cameras in the network and asynchronous parallel processing;
3. Communication Optimization and minimization of the
(battery-stored) energy, by reducing and localizing the
communications between cameras. A volumetric
representation of the scene is reconstructed with a shape from
apparent contour algorithm, which is suitable for distributed
processing because it is essentially a local operation in terms of
the involved cameras, and apparent contours are robust to our-door illumination conditions.
Each camera processes its own image and
performs the computation for a small subset of voxels, and updates
the voxels through collaborating with its neighbor cameras. By
exploring the structure of the reconstruction algorithm, we design
the minimum-spanning-tree (MST) message passing protocol in order to
minimize the communication. Of interest is that the resulting system
is an example of ``swarm behavior''. 3D reconstruction is
illustrated using two real image sets, running on a single
computer. The iterative computations used in the single
processor experiment are exactly the same as are those used in the
network computations. Distributed concepts and algorithms for
network control and communication performance are theoretical
designs and estimates.
Reference
@INPROCEEDINGS{jpt-cvpr09,
author = {Liu, Shubao and Kang, Kongbin and Tarel, Jean-Philippe and Cooper, David B.},
title = {Distributed Volumetric Scene Geometry Reconstruction With a Network of Distributed Smart Cameras},
booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09)},
address = {Miami Beach, Florida, USA},
date = {June 20-25},
pages = {2334-2341},
year = {2009},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/cvpr09.html}
}
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