3D Scene Modeling using Pose-free Reconstruction
Summary
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Reconstructing large models from images
is a significant challenge for computer graphics, computer vision, and
related fields. In this project, we investigate an approach for simplifying
the reconstruction process by mathematically eliminating external camera
parameters. This results in less parameters to estimate and in an
overall significantly more robust and accurate reconstruction. Unlike
self-calibration, omitting pose parameters from the acquisition process
implies no external calibration data must be computed or provided. We
reformulate the problem in such a manner as to be able to identify
invariants, eliminate superfluous parameters, and measure the
performance of our formulation under various conditions. We compare a
two-step camera orientation-free method, where the majority of the
points are reconstructed using a linear equation set, and a camera
position-and-orientation free method, using a degree-two equation set.
Aside from freely taking pictures and moving an uncalibrated digital
projector, scene acquisition and scene point reconstruction is automatic
and requires pictures from only a few viewpoints. We demonstrate how the
combination of these benefits has enabled us to acquire several large
and detailed models. |
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Comparison of all Methods. Using an experiment with known ground truth, we
compare pose-included methods to our orientation-free and pose-free
methods. In all cases our method exhibits significantly higher
robustness and accuracy. |
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Orientation-Free Reconstructions. We show several reconstructions of the
giraffe dataset using our orientation-free method (bottom row) and using
standard pose-included bundle adjustment (top row). The error of the
different reconstructions is shown by drawing lines between the
reconstructed points and the highest quality reconstruction (e.g.,
ground truth). (top-left) Example input image from giraffe sequence.
(left-bottom) High-quality reconstruction using our method. (top-middle)
Medium-quality solution using standard BA. (bottom-middle)
Medium-quality solution using our method. (top-right) Low-quality
solution using standard BA. (bottom-right) Low-quality solution using
our method. |
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Comparison to Pose-Included for
Large Datasets. We show the reconstruction error using our two
largest datasets for both our pose-free formulation and for
pose-included formulation. Equal amounts of structure and pose error are
introduced to create this graph. Values are expressed as a percentage of
the 10-meter model diagonal of the models. |
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Scene Reconstruction. The top row
shows reconstructed scene points and the bottom row contains
texture-mapped triangulations of the same scene points., both using our
completely pose-free formulation. The scene points for both clusters
were solved for in a single reconstruction. (left column) Reconstruction
of cluster A. (middle column) Reconstruction of cluster B. (right
column) Both clusters rendered together. |
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Global Multi-viewpoint Model. Our
method enables easy, robust, and accurate capturing of large scenes
assembled from multiple acquisitions (left) in a single global
reconstruction (top). Our approach produces texture-mapped geometric
models and captures dense and high-detailed scene information. |
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Writeups:
D. Aliaga, J. Zhang, M. Boutin, "3D Scene Modeling
using Pose-free Reconstruction", submitted for publication, 2007.
PDF
VIDEO
D. Aliaga, J. Zhang, M. Boutin, "Robust and Globally-Consistent 3D Reconstruction for Acquisition and Registration
", submitted for publication, 2007.
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