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dc.contributor.authorYao, Yikai 17:03:20 (GMT) 17:03:20 (GMT)
dc.description.abstractAs 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.publisherUniversity of Waterlooen
dc.subjectpoint clouden
dc.subjectobject detectionen
dc.titleUpsampling Indoor LiDAR Point Clouds for Object Detectionen
dc.typeMaster Thesisen
dc.pendingfalse and Environmental Managementen of Waterlooen
uws-etd.degreeMaster of Scienceen
uws.contributor.advisorLi, Jonathan
uws.contributor.affiliation1Faculty of Environmenten

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