SSPU-Net: A Structure Sensitive Point Cloud Upsampling Network with Multi-Scale Spatial Refinement

Point cloud upsampling aims to generate a dense and uniform point set from a sparse and irregular point set. The core challenge is to accurately restore the geometric structure and local details. To overcome the challenge, this paper presents a novel frequency-aware attention based point cloud upsampling approach, which combines graph filtering and channel attention based on the detection of high spatial-frequency components like edges and contours in the human visual system. To aggregate the features more efficiently, an intra-feature and inter-feature (I-2-feature) aggregation block and a structure sensitive transformer block are introduced. On one hand, the I-2-feature aggregation block serves to create a complete local representation of each point by aggregating intra and inter features. On the other hand, the structure sensitive transformer block aims to enhance the quality of the expanded point features by capturing the global geometric structures and the fine local details. Furthermore, to improve the quality of the coarse output, a multi-scale spatial refinement unit is applied, which leverages attentional feature fusion and multi-scale attention. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets validate our proposed scheme outperforms state-of-the-art point cloud upsampling methods.