Upsampling Indoor LiDAR Point Clouds for Object Detection

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.