Self-Supervised Implicit Attention Priors for Point Cloud Reconstruction

The recent publication on self-supervised implicit attention priors for point cloud reconstruction presents a novel method that addresses the challenges of recovering high-quality surfaces from irregular point clouds. By leveraging an implicit self-prior approach, the technique distills shape-specific priors directly from the input data, allowing for improved surface recovery without the need for external training datasets. This is achieved through the joint training of a small dictionary of learnable embeddings alongside an implicit distance field, which utilizes cross-attention to capture repeating structures and long-range correlations inherent in the shape. The method is optimized using self-supervised point cloud reconstruction losses and integrates the learned prior into a robust implicit moving least squares (RIMLS) formulation. The results indicate that this hybrid strategy not only preserves fine geometric details but also outperforms both classical and learning-based approach…