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View-Agnostic Point Cloud Generation

Singer, Nina
http://orcid.org/0000-0001-5916-6425
http://rave.ohiolink.edu/etdc/view?acc_num=dayton165027519839772

Abstract Details

2022, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical and Computer Engineering.
Occlusions are one of the primary data challenges when working with lidar. Unfortunately, occlusions are highly dependent on sensor viewpoints, and efforts to mitigate occlusions involve costly data collection strategies like additional overlap or multiple views. This research focuses on reducing occlusions by generating the missing points in a post-processing step. We introduce an entirely new occlusion dataset for aerial lidar called DALES Viewpoints. We also propose two fundamental changes that we can use in conjunction with current point cloud completion networks to provide an appropriate solution for occlusion reduction in aerial lidar. Specifically, we propose a new method of Eigen feature selection for hierarchical downsampling. This method takes into account point features, in addition to spatial location. We also introduce a point correspondence loss that helps build more robust features by ensuring similar network behavior when processing point clouds that depict the same scene with different physical point locations.
Vijayan Asari (Committee Chair)
David Rabb (Committee Member)
Keigo Hirakawa (Committee Member)
Theus Aspiras (Committee Member)
115 p.
Artificial Intelligence; Computer Science; Statistics
lidar; aerial lidar; 3d; point clouds; occlusion; autoencoder

Recommended Citations

Citations

  • Singer, N. (2022). View-Agnostic Point Cloud Generation [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton165027519839772

    APA Style (7th edition)

  • Singer, Nina. View-Agnostic Point Cloud Generation. 2022. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton165027519839772.

    MLA Style (8th edition)

  • Singer, Nina. "View-Agnostic Point Cloud Generation." Doctoral dissertation, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton165027519839772

    Chicago Manual of Style (17th edition)

dayton165027519839772
59
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This open access ETD is published by University of Dayton and OhioLINK.