|M.Sc Student||Elbaz Gil|
|Subject||3D Point Cloud Registration for Localization Using a|
Deep Neural Network Auto-Encoder
|Department||Department of Mechanical Engineering||Supervisor||Professor Anath Fischer|
|Full Thesis text|
Presented is an algorithm for registration between a large-scale point cloud
and a close-proximity scanned point cloud, providing a localization solution that
is fully independent of prior information about the initial positions of the two
point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points?local subsets of points?and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential
matching regions for an efficient coarse registration process, followed by a fine-tuning
stage. The set of super-points is selected by covering the point clouds with
overlapping spheres, and then filtering out those of low-quality or nonsalient regions.
The descriptors are computed using state-of-the-art unsupervised machine
learning, utilizing the technology of deep neural network based auto-encoders.
This novel framework provides a strong alternative to the common practice of
using manually designed key-point descriptors for coarse point cloud registration.
Utilizing super-points instead of key-points allows the available geometrical data
to be better exploited to find the correct transformation. Encoding local 3D geometric
structures using a deep neural network auto-encoder instead of traditional
descriptors continues the trend seen in other computer vision applications and indeed
leads to superior results. The algorithm is tested on challenging point cloud
registration datasets, and its advantages over previous approaches as well as its
robustness to density changes, noise and missing data are shown.