The field of data representation and visualization is rapidly evolving, with a focus on improving performance, efficiency, and accuracy. Recent developments have centered around the creation of innovative methods for selecting and generating meshes, sampling point clouds, and visualizing data. One key direction is the use of learning-based approaches to address challenges such as ensuring coplanarity, convexity, and quad-only meshes in geometric modeling. Another area of research is the development of shape-specific sampling strategies that balance local detail and global uniformity in point cloud data. Perception-aware sampling methods are also being explored to improve the effectiveness of scatterplot visualizations. Notable papers in this area include:

• Point2Quad, which presents a learning-based method for quad-only mesh generation from point clouds.
• SAMBLE, which proposes a Sparse Attention Map and Bin-based Learning method to learn shape-specific sampling strategies for point cloud shapes.
• Perception-aware Sampling for Scatterplot Visualizations, which introduces a novel sampling objective aiming to improve sample efficacy by targeting humans' perception of a visualization.