  Sampling is widely used in various point cloud tasks as it can effectively
reduce resource consumption. Recently, some methods have proposed utilizing
neural networks to optimize the sampling process for various task requirements.
Currently, deep downsampling methods can be categorized into two main types:
generative-based and score-based. Generative-based methods directly generate
sampled point clouds using networks, whereas score-based methods assess the
importance of points according to specific rules and then select sampled point
clouds based on their scores. However, these methods often result in noticeable
clustering effects in high-intensity feature areas, compromising their ability
to preserve small-scale features and leading to the loss of some structures,
thereby affecting the performance of subsequent tasks. In this paper, we
propose REPS, a reconstruction-based scoring strategy that evaluates the
importance of each vertex by removing and reconstructing them using surrounding
vertices. Our reconstruction process comprises point reconstruction and shape
reconstruction. The two aforementioned reconstruction methods effectively
evaluate the importance of vertices by removing them at different scales for
reconstruction. These reconstructions ensure that our method maintains the
overall geometric features of the point cloud and avoids disturbing small-scale
structures during sampling. Additionally, we propose the Global-Local Fusion
Attention (GLFA) module, which aggregates local and global attention features
of point clouds, ensuring high-quality reconstruction and sampling effects. Our
method outperforms previous approaches in preserving the structural features of
the sampled point clouds. Furthermore, abundant experimental results
demonstrate the superior performance of our method across various common tasks.
