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.

翻译：三维点云数据的可视化在自主导航、环境监测和灾害响应等领域至关重要，这些领域中的物体识别、结构分析和时空探索等任务都依赖于清晰有效的视觉呈现。尽管人工智能驱动的处理技术取得了进展，可视化仍然是解释复杂空间数据集的关键工具。然而，由于数据的稀疏性、密度变化和规模，设计有效的点云可视化面临着重大挑战。在本工作中，我们分析了空间点云可视化的设计空间，指出在将可视化技术系统性地映射到分析目标方面存在空白。我们提出了一种分类法，对过去四十年的可视化设计选择进行分类，并将其与现代应用中的基本挑战联系起来。通过基于数据类型、用户目标和可视化技术来构建可视化策略，我们的框架为推进更有效、可解释且以用户为中心的可视化技术奠定了基础。