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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Lin Zhao Siyuan Xu Liman Liu Delie Ming Wenbing Tao |
| Abstract | 3D LiDAR has become an indispensable sensor in autonomous driving vehicles. In LiDAR-based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot efficiently capture large amounts of context information, resulting in limited receptive fields and limiting their performance. To address this problem, a sparse voxel-based attention network is introduced for 3D LiDAR point cloud semantic segmentation, termed SVASeg, which captures large amounts of context information between voxels through sparse voxel-based multi-head attention (SMHA). The traditional multi-head attention cannot directly be applied to the non-empty sparse voxels. To this end, a hash table is built according to the incrementation of voxel coordinates to lookup the non-empty neighboring voxels of each sparse voxel. Then, the sparse voxels are grouped into different groups, and each group corresponds to a local region. Afterwards, position embedding, multi-head attention and feature fusion are performed for each group to capture and aggregate the context information. Based on the SMHA module, the SVASeg can directly operate on the non-empty voxels, maintaining a comparable computational overhead to the convolutional method. Extensive experimental results on the SemanticKITTI and nuScenes datasets show the superiority of SVASeg. |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14184471 |
| Journal | Remote Sensing |
| Issue Number | 4471 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI AG |
| Publisher Date | 2022-09-01 |
| Publisher Place | Switzerland |
| Access Restriction | Open |
| Subject Keyword | Science Sparse Voxel-based Attention Lidar Point Cloud Semantic Segmentation Svaseg |
| Content Type | Text |
| Resource Type | Article |
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