Annotating 3D point cloud data is labor-intensive. Self-supervised representation learning can reduce the intense demand of manual annotation. However, the sparsity of point cloud, while containing ...
Point cloud analysis is integral to numerous applications, including mapping and autonomous driving. However, the unstructured and disordered nature of point clouds presents significant challenges for ...
The task of point cloud classification suffers from the problem of insufficient data, and data augmentation is an effective method to alleviate this problem. However, the effect of conventional ...
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