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Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation
| Content Provider | MDPI |
|---|---|
| Author | Geng, Xiaoxiao Ji, Shunping Lu, Meng Zhao, Lingli |
| Copyright Year | 2021 |
| Description | Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and augmented reality, among others. In this paper, we propose a Multi-Scale Attentive Aggregation Network (MSAAN) to achieve the global consistency of point cloud feature representation and super segmentation performance. First, upon a baseline encoder-decoder architecture for point cloud segmentation, namely, RandLA-Net, an attentive skip connection was proposed to replace the commonly used concatenation to balance the encoder and decoder features of the same scales. Second, a channel attentive enhancement module was introduced to the local attention enhancement module to boost the local feature discriminability and aggregate the local channel structure information. Third, we developed a multi-scale feature aggregation method to capture the global structure of a point cloud from both the encoder and the decoder. The experimental results reported that our MSAAN significantly outperformed state-of-the-art methods, i.e., at least 15.3% mIoU improvement for scene-2 of CSPC dataset, 5.2% for scene-5 of CSPC dataset, and 6.6% for Toronto3D dataset. |
| Starting Page | 691 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13040691 |
| Journal | Remote Sensing |
| Issue Number | 4 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-02-14 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Lidar Point Cloud Segmentation Attentive Skip Connection Channel Attentive Enhancement Multi-scale Aggregation Deep Learning |
| Content Type | Text |
| Resource Type | Article |
