# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 2 Related Work  
The problem of visualizing multidimensional data has inspired a diverse set of techniques, each with its strengths and limitations. Projection-based methods, such as Principal Component Analysis (PCA) [7], t-distributed stochastic neighbor embedding (t-SNE) [8], and Uniform Manifold Approximation and Projection (UMAP) [9], are commonly used to reduce high-dimensional datasets to two or three dimensions, facilitating the discovery of patterns, clusters, or outliers.  
Coordinate-based visualizations, such as parallel coordinates [10] and radial plots [11], allow each variable to be explicitly represented as an axis. Parallel coordinates, in particular, have been widely used in medicine (e.g., \"liver profiles\"), and their variants, such as Andrews plots [12], provide additional smoothing and compactness. These approaches are effective for moderate numbers of dimensions but can become visually cluttered as dimensionality increases.  
Matrix-based and small multiples techniques, including trellis charts [13], offer compact overviews of pairwise relationships between variables, enabling users to examine a large number of possible interactions.  
Glyph-based approaches, most notably Chernoff faces [1], map multiple variables onto visual features of an icon or avatar, leveraging innate human abilities for face and pattern recognition. However, the subjectivity and emotional connotations of human-like glyphs can bias interpretation, and most glyph-based methods are static and limited to two dimensions.  
Dynamic and interactive exploration techniques have also been developed, such as the grand tour and guided tour [14] and the slice tour [15]. These methods present users with sequences of projections or thin slices through the data space, sometimes augmented with glyphs for detailed inspection (e.g., the tour package for R, version 1.2.4). While powerful for uncovering high-dimensional structures, these approaches typically treat all variables as equivalent, making them challenging for non-experts to interpret and understand.  
More recently, cognitively grounded approaches have been explored. [PERSON] et al. [16] introduced \"data avatars\"--metaphorical glyphs designed to visually encode multidimensional data in ways that align with intuitive human perception. Their work, based on dual-process cognitive theory, demonstrated that such designs could improve interpretation; however, they did not address interactive navigation or explicit semantic division of variables.  
Compared to the above methods, our approach introduces two key innovations. First, we enable a semantic split of the dataset into two groups: dimensions that are intuitively meaningful to users (such as sensory or quality ratings) and those that are more technical or abstract (such as chemical or process parameters). The intuitive features are mapped to avatar visual traits (inspired by [PERSON] faces), while the technical features are projected into spatial coordinates, optionally using dimensionality reduction (e.g., PCA). Second, we integrate these glyphs into an interactive 3D environment, allowing users to explore a \"swarm\" of avatars, dynamically rotate the viewpoint, and apply slab-based filtering to reveal local structure.  
Our slab filtering is conceptually related to the slice tour [15], as both select points near a hyperplane for visualization. However, while the slice tour typically focuses on very thin slices (showing only points lying almost exactly on a hyperplane), our approach uses a slab of configurable thickness, retaining all points within a certain distance from the projection plane. This design supports the practical exploration of moderately sized datasets, reducing visual clutter while still revealing structural patterns. To our knowledge, this is the first system to combine avatar-based glyphs with interactive 3D navigation, explicit semantic variable assignment, and flexible local filtering in this manner.