Network architecture. First, FPS is used to downsample the input light curve sampling points in order to determine the center points. Then, the nearest neighbor set corresponding to each center point is obtained with the KNN algorithm, and local features are extracted using a simplified 1D-DGCNN. Afterwards, positional embeddings are added to enhance spatial awareness. The enhanced features are encoded by a geometry-aware transformer module followed by global feature aggregation through max pooling. Finally, the coarse point cloud is refined into the output using a transformer decoder that integrates three up-sampling layers and skip connections.