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Visualizing Spatial Point Clouds: A Task-Oriented Taxonomy

Mahsa Partovi, Federico Iuricich

TL;DR

The paper addresses the challenge of effectively visualizing spatial point clouds by proposing a task-oriented taxonomy grounded in the What-Why-How framework. It synthesizes four decades of literature to map data abstractions, user tasks, and design choices across point-based, grid-based, and geometry-based visualizations, highlighting where techniques align with or fail to support low-level perceptual tasks. Key contributions include a structured taxonomy, identification of data-type and task-specific visualization strategies, and a discussion of gaps in evaluation and human-centered design. The framework aims to guide future research toward perception-aware, task-driven visualization methods that balance cognitive effectiveness with computational efficiency in real-world spatial data analysis.

Abstract

The visualization of 3D point cloud data is essential in fields such as autonomous navigation, environmental monitoring, and disaster response, where tasks like object recognition, structural analysis, and spatiotemporal exploration rely on clear and effective visual representation. Despite advancements in AI-driven processing, visualization remains a critical tool for interpreting complex spatial datasets. However, designing effective point cloud visualizations presents significant challenges due to the sparsity, density variations, and scale of the data. In this work, we analyze the design space of spatial point cloud visualization, highlighting a gap in systematically mapping visualization techniques to analytical objectives. We introduce a taxonomy that categorizes four decades of visualization design choices, linking them to fundamental challenges in modern applications. By structuring visualization strategies based on data types, user objectives, and visualization techniques, our framework provides a foundation for advancing more effective, interpretable, and user-centered visualization techniques.

Visualizing Spatial Point Clouds: A Task-Oriented Taxonomy

TL;DR

The paper addresses the challenge of effectively visualizing spatial point clouds by proposing a task-oriented taxonomy grounded in the What-Why-How framework. It synthesizes four decades of literature to map data abstractions, user tasks, and design choices across point-based, grid-based, and geometry-based visualizations, highlighting where techniques align with or fail to support low-level perceptual tasks. Key contributions include a structured taxonomy, identification of data-type and task-specific visualization strategies, and a discussion of gaps in evaluation and human-centered design. The framework aims to guide future research toward perception-aware, task-driven visualization methods that balance cognitive effectiveness with computational efficiency in real-world spatial data analysis.

Abstract

The visualization of 3D point cloud data is essential in fields such as autonomous navigation, environmental monitoring, and disaster response, where tasks like object recognition, structural analysis, and spatiotemporal exploration rely on clear and effective visual representation. Despite advancements in AI-driven processing, visualization remains a critical tool for interpreting complex spatial datasets. However, designing effective point cloud visualizations presents significant challenges due to the sparsity, density variations, and scale of the data. In this work, we analyze the design space of spatial point cloud visualization, highlighting a gap in systematically mapping visualization techniques to analytical objectives. We introduce a taxonomy that categorizes four decades of visualization design choices, linking them to fundamental challenges in modern applications. By structuring visualization strategies based on data types, user objectives, and visualization techniques, our framework provides a foundation for advancing more effective, interpretable, and user-centered visualization techniques.

Paper Structure

This paper contains 26 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Scope and Method
  3. Data abstraction (What)
  4. Point cloud attributes
  5. Task Abstraction (Why)
  6. Parts-to-whole relations
  7. Spatial relationships
  8. Temporal Relationships
  9. Design choices (How)
  10. Point-based designs
  11. Sampling techniques
  12. Filtering
  13. Resampling
  14. Relations with data types and tasks
  15. Visualization of attributes
  16. ...and 11 more sections

Figures (3)

  • Figure 1: Our taxonomy categorizes low-level user tasks into three main groups: Parts-to-Whole, where the focus is on understanding structural characteristics from a collection of points; Spatial relations, which involves evaluating the spatial relationships between points or objects; and Temporal relations, where the goal is to analyze changes in objects over time.
  • Figure 2: Visual encodings that can be used for modifying the appearance of points based on codomain attributes.
  • Figure 3: Geometry based techniques organized in three subcategories, annotations, lines, and surfaces.