Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder-Decoder Shared MLPs with Multiple Lossesopen access
- Authors
- Rim, Beanbonyka; Lee, Ahyoung; Hong, Min
- Issue Date
- Aug-2021
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Keywords
- semantic segmentation; 3D LiDAR point clouds; deep learning; remote sensing
- Citation
- Remote Sensing, v.13, no.16
- Journal Title
- Remote Sensing
- Volume
- 13
- Number
- 16
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19858
- DOI
- 10.3390/rs13163121
- ISSN
- 2072-4292
- Abstract
- Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder-decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder-decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19858)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.