THESIS
2017
xvii, 111 pages : illustrations ; 30 cm
Abstract
Structure from motion is a photogrammetric technique for estimating 3D structures from
2D images and large-scale Structure from Motion is still challenging in three aspects, namely
accuracy, scalability and efficiency. The target of this work is to handle highly accurate and
consistent large-scale Structure from Motion problems in a parallel and scalable manner.
First, we propose a scalable distributed formulation to handle Structure from Motion problems
far exceeding the memory of a single computer in parallel. Different from the previous
methods which drastically simplify the parameters of Structure from Motion, we propose a
camera clustering algorithm to divide a large Structure from Motion problem into smaller sub-problems
in terms of camera clusters with overlapping while p...[
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Structure from motion is a photogrammetric technique for estimating 3D structures from
2D images and large-scale Structure from Motion is still challenging in three aspects, namely
accuracy, scalability and efficiency. The target of this work is to handle highly accurate and
consistent large-scale Structure from Motion problems in a parallel and scalable manner.
First, we propose a scalable distributed formulation to handle Structure from Motion problems
far exceeding the memory of a single computer in parallel. Different from the previous
methods which drastically simplify the parameters of Structure from Motion, we propose a
camera clustering algorithm to divide a large Structure from Motion problem into smaller sub-problems
in terms of camera clusters with overlapping while preserving as many connectivity
among cameras and tracks as possible. We next exploit a hybrid formulation using the relative
motions from local incremental Structure from Motion into a global motion averaging
framework to produce superior accurate and consistent initial camera poses. Our scalable formulation
in terms of camera clusters is highly applicable to the whole Structure from Motion
pipeline including track generation, local Structure from Motion, 3D point triangulation and
bundle adjustment.
To achieve scalable distributed motion averaging, we base on the scalable formulation
in terms of camera clusters to decouple the full motion averaging problem into several sub-problems
with respect to their local coordinate frames encoded by similarity transformations
for independent optimization in parallel. Then, we can merge sub-problems globally without
caching the whole reconstruction in memory at once. The proposed distributed and robust
framework supplements the majority of the state-of-the-art motion averaging approaches with superior improvement in efficiency and robustness.
As for large-scale bundle adjustment, eliminating statistical redundancy in multi-view geometry
is of great importance to efficient 3D reconstruction. Our approach takes the full set of
images with initial calibration and recovered sparse 3D points as inputs, and obtains a subset of
views that preserve the final reconstruction accuracy and completeness well. Moreover, global
bundle adjustment usually converges to a non-zero residual and produces sub-optimal camera
poses for local areas, which leads to loss of details for high-resolution reconstruction. Instead
of trying harder to optimize everything globally, we argue that we should live with the non-zero
residual and adapt the camera poses to local areas. To this end, we propose a segment-based
approach to readjust the camera poses locally and improve the reconstruction for fine geometry
details. This significantly reduces severe propagated errors and estimation biases caused by the initial global adjustment.
Together these techniques enable the first pipeline able to reconstruct highly accurate and
consistent camera poses from more than one million high-resolution images in parallel with the
state-of-the-art accuracy and robustness evaluated on both the benchmark, Internet and challenging
city-scale data-sets.
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