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.
Highlights
The 3D LiDAR point clouds have become one of the most significant 3D data presentations for depth information, and have been deployed in various applications, such as urban geometry mapping, autonomous driving, virtual reality, augmented reality, and more [1,2,3]
Effective, strategy of the above aforementioned mechanisms, such as a random point sampling, attention-based pooling, and multiple losses summation integrated with the encoder–decoder shared multi-layer perceptron (MLP) method, for the large-scale outdoor point clouds semantic segmentation; We proof that our method performs good results and has a lower computational cost than PointNet++ [11]
For experiment on the xyz coordinates of the points, our proposed method achieved an overall accuracy (OA)
Summary
The 3D LiDAR point clouds have become one of the most significant 3D data presentations for depth information, and have been deployed in various applications, such as urban geometry mapping, autonomous driving, virtual reality, augmented reality, and more [1,2,3]. Point cloud is a set of points in a 3D metric space, which provides rich 3D information, such as geometry, color, intensity, normal, and more, to accurately measure the surrounding objects. This information can be utilized for scene understanding. Among tasks that are related to the point cloud scene understanding, a semantic segmentation is a task that has the role of assigning each point to a meaningful label. This means that it does tell the location of the object, but it describes what kind of object is in the scene. We propose an outdoor 3D point clouds semantic segmentation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
