Upsampling Indoor LiDAR Point Clouds for Object Detection
| dc.contributor.author | Yao, Yikai | |
| dc.date.accessioned | 2023-05-24T17:03:20Z | |
| dc.date.available | 2023-05-24T17:03:20Z | |
| dc.date.issued | 2023-05-24 | |
| dc.date.submitted | 2023-05-10 | |
| dc.description.abstract | As an emerging technology, LiDAR point cloud has been applied in a wide range of fields. With the ability to recognize and localize the objects in a scene, point cloud object detection has numerous applications. However, low-density LiDAR point clouds would degrade the object detection results. Complete, dense, clean, and uniform LiDAR point clouds can only be captured by high-precision sensors which need high budgets. Therefore, point cloud upsampling is necessary to derive a dense, complete, and uniform point cloud from a noisy, sparse, and non-uniform one. To address this challenge, we proposed a methodology of utilizing point cloud upsam pling methods to enhance the object detection results of low-density point clouds in this thesis. Specifically, we conduct three point cloud upsampling methods, including PU-Net, 3PU, and PU-GCN, on two datasets, which are a dataset we collected on our own in an underground parking lot located at Highland Square, Kitchener, Canada, and SUN-RGBD. We adopt VoteNet as the object detection network. We subsampled the datasets to get a low-density dataset to stimulate the point cloud captured by the low-budget sensors. We evaluated the proposed methodology on two datasets, which are SUN RGB-D and the collected underground parking lot dataset. PU-Net, 3PU, and PU-GCN increase the mean Average Precision (under the threshold of 0.25) by 18.8%,18.0%, and 18.7% on the underground parking lot dataset and 9.8%, 7.2%, and 9.7% on SUN RGB-D. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/19478 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.subject | point cloud | en |
| dc.subject | LiDAR | en |
| dc.subject | upsampling | en |
| dc.subject | object detection | en |
| dc.title | Upsampling Indoor LiDAR Point Clouds for Object Detection | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Science | en |
| uws-etd.degree.department | Geography and Environmental Management | en |
| uws-etd.degree.discipline | Geography | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 0 | en |
| uws.contributor.advisor | Li, Jonathan | |
| uws.contributor.affiliation1 | Faculty of Environment | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |