The field of mesh generation and clustering is witnessing significant advancements, with a focus on improving computational efficiency, robustness, and adaptability to complex data structures. Researchers are exploring novel approaches to density peaks clustering, mesh tokenization, and point cloud completion, aiming to overcome limitations in existing methods and achieve superior performance. Notable developments include the integration of granular-ball computing with density peaks clustering, the introduction of new metrics and tokenization techniques for mesh generation, and the proposal of flexible-weighted objective functions for point cloud completion. These innovations have the potential to enhance the accuracy, efficiency, and applicability of mesh generation and clustering algorithms in various domains. Noteworthy papers include: LGBQPC, which proposes a local granular-ball quality peaks clustering algorithm to improve computational efficiency and robustness. FreeMesh, which introduces a new metric and tokenization technique to enhance mesh generation. Flexible-weighted Chamfer Distance, which proposes a novel objective function to improve point cloud completion. A Remeshing Method via Adaptive Multiple Original-Facet-Clipping and Centroidal Voronoi Tessellation, which presents a CVT-based remeshing approach to optimize mesh quality and efficiency. Mesh-RFT, which employs a fine-grained reinforcement fine-tuning framework to improve mesh generation quality.