THESIS
2020
Abstract
Surface reconstruction, also known as mesh reconstruction, is a critical step in 3D reconstruction
pipeline that generates 3D models from a series of images of a specific objects.
The input of the surface reconstruction is a sparse 3D point cloud obtained from
Structure-from-Motion (SfM) and the output is a 3D model represented by a mesh. In
general, this process is implemented off-line because it requires a cost calculation of optimizing
labelling energy and cannot insert or remove points dynamically. In this thesis, a
new reconstruction method is implemented which can incrementally extract surface from
tetrahedra after the triangulation from streaming point cloud. A novel energy function is
harnessed in order to reduce the time complexity and without losing the accuracy compa...[
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Surface reconstruction, also known as mesh reconstruction, is a critical step in 3D reconstruction
pipeline that generates 3D models from a series of images of a specific objects.
The input of the surface reconstruction is a sparse 3D point cloud obtained from
Structure-from-Motion (SfM) and the output is a 3D model represented by a mesh. In
general, this process is implemented off-line because it requires a cost calculation of optimizing
labelling energy and cannot insert or remove points dynamically. In this thesis, a
new reconstruction method is implemented which can incrementally extract surface from
tetrahedra after the triangulation from streaming point cloud. A novel energy function is
harnessed in order to reduce the time complexity and without losing the accuracy comparing
to state-of-the-art method. By utilizing this energy function and dynamic version of
graph cut algorithm, the reconstruction process can be achieved in real-time and dynamic
manner. With this implementation, 3D reconstruction can be applied in the real-time
applications such as Simultaneous Localization and Mapping (SLAM).
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