Miao, Y.; Ren, G.; Zhang, X.; Liu, H.; Liu, F. Weakly Supervised Semantic Segmentation of Point Cloud Scenes using Boundary-based Feature Aggregation. Preprints2024, 2024040038. https://doi.org/10.20944/preprints202404.0038.v1
APA Style
Miao, Y., Ren, G., Zhang, X., Liu, H., & Liu, F. (2024). Weakly Supervised Semantic Segmentation of Point Cloud Scenes using Boundary-based Feature Aggregation. Preprints. https://doi.org/10.20944/preprints202404.0038.v1
Chicago/Turabian Style
Miao, Y., Haijian Liu and Fuchang Liu. 2024 "Weakly Supervised Semantic Segmentation of Point Cloud Scenes using Boundary-based Feature Aggregation" Preprints. https://doi.org/10.20944/preprints202404.0038.v1
Abstract
The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.
Keywords
Point cloud; Semantic segmentation; Weakly supervised learning; Boundary feature aggregation; Data augmentation
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
